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#CVPR2026 Can frontier LLMs write PhD-level 3D vision code? We introduce GeoCodeBench, a benchmark that asks models to read real 3D geometric vision papers and implement core functions. Best result so far: GPT-5 reaches only 36.6%. This suggests that scientific coding in 3D vision remains far from solved. Paper: Project:

Hao Zhao

1,422 subscribers

18,324 次观看 • 4 个月前 •via X (Twitter)

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4 条评论

Vlad Erium 🇯🇵 的头像

Vlad Erium 🇯🇵4 个月前

GPT-5?

EB1A Experts 的头像

EB1A Experts4 个月前

Great work!

Bang-Dang Pham ✈️ CVPR26 的头像

Bang-Dang Pham ✈️ CVPR264 个月前

@CSProfKGD Could you share how can you create this slide :)))

Elodie 3DFontaine 的头像

Elodie 3DFontaine4 个月前

Renderings are pretty, but let me see the geometric logic. I don't want to remix a shadow because an LLM can't handle feature topology.

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3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,798 次观看 • 3 年前

LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.

LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.

AK

41,747 次观看 • 2 年前

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision paper page: We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation.

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision paper page: We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation.

AK

49,929 次观看 • 2 年前

They finally did it! YouTube is now an official app for Apple Vision Pro and can be downloaded from the App Store. It features 3D, 180 and 360-degree content and an optimised user interface. So far I really like it. Still, it feels surreal that this is actually happening.

They finally did it! YouTube is now an official app for Apple Vision Pro and can be downloaded from the App Store. It features 3D, 180 and 360-degree content and an optimised user interface. So far I really like it. Still, it feels surreal that this is actually happening.

Phil Traut ᯅ

74,922 次观看 • 7 个月前

Humans draw to facilitate reasoning and communication. Why not let LLMs do so? 🚀We introduce✏️Sketchpad, which gives multimodal LLMs a sketchpad to draw and facilitate reasoning! Sketchpad gives GPT-4o great boosts on many vision and math tasks 📈 The video shows how GPT-4o with Sketchpad reasons with interleaved visual and textual steps. For more, visit our project page: 📌 For math tasks, ✏️Sketchpad allows LLMs to draw auxiliary lines on geometry diagrams, plotting functions, graphs, and even games. GPT-4o does math better when it can sketch! (+12.7% acc on average) 📌 For computer vision tasks, ✏️Sketchpad allows LLMs to sketch with vision specialists (e.g., GroundingDINO draws bounding boxes, SegmentAnything draws masks). Sketchpad substantially improves GPT-4o's vision abilities. GPT-4o + Sketchpad compared with prior SOTAs: 1️⃣ V*Bench: 75.4% -> 80.3% 2️⃣ BLINK correspondence: 42.4% -> 80.8% 3️⃣ BLINK relative depth: 67.7% -> 83.9% 4️⃣ BLINK spatial relation: 76.2% -> 81.1% ... See more interesting examples in the thread!

Humans draw to facilitate reasoning and communication. Why not let LLMs do so? 🚀We introduce✏️Sketchpad, which gives multimodal LLMs a sketchpad to draw and facilitate reasoning! Sketchpad gives GPT-4o great boosts on many vision and math tasks 📈 The video shows how GPT-4o with Sketchpad reasons with interleaved visual and textual steps. For more, visit our project page: 📌 For math tasks, ✏️Sketchpad allows LLMs to draw auxiliary lines on geometry diagrams, plotting functions, graphs, and even games. GPT-4o does math better when it can sketch! (+12.7% acc on average) 📌 For computer vision tasks, ✏️Sketchpad allows LLMs to sketch with vision specialists (e.g., GroundingDINO draws bounding boxes, SegmentAnything draws masks). Sketchpad substantially improves GPT-4o's vision abilities. GPT-4o + Sketchpad compared with prior SOTAs: 1️⃣ V*Bench: 75.4% -> 80.3% 2️⃣ BLINK correspondence: 42.4% -> 80.8% 3️⃣ BLINK relative depth: 67.7% -> 83.9% 4️⃣ BLINK spatial relation: 76.2% -> 81.1% ... See more interesting examples in the thread!

Yushi Hu

145,242 次观看 • 2 年前

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Abdullah Hamdi

71,661 次观看 • 1 年前

How far can a very simple eye go in solving vision tasks? Like a 1-pixel camera? Humans have one of the greatest eyes in nature, while many animals have significantly simpler eyes and visual systems yet show complex perceptual behavior. In an interesting project, we find that many computer vision tasks can be solved without a typical camera and with such simple 1-pixel sensors (photoreceptors). We also find that proper design (e.g., where to place the photoreceptors strategically) makes a big difference, so we developed a computational design method to find them. 🌐 👁️[Solving Vision Tasks with Simple Photoreceptors Instead of Cameras] 🧵1/n

How far can a very simple eye go in solving vision tasks? Like a 1-pixel camera? Humans have one of the greatest eyes in nature, while many animals have significantly simpler eyes and visual systems yet show complex perceptual behavior. In an interesting project, we find that many computer vision tasks can be solved without a typical camera and with such simple 1-pixel sensors (photoreceptors). We also find that proper design (e.g., where to place the photoreceptors strategically) makes a big difference, so we developed a computational design method to find them. 🌐 👁️[Solving Vision Tasks with Simple Photoreceptors Instead of Cameras] 🧵1/n

Amir Zamir

75,908 次观看 • 2 年前

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We finally have a product that helps with frontier AI research. Frontier LLMs are a) nerfed at AI research b) lack immediate GPU availability and c) lack access to the right skills and research material. "Teach me how vision models see" Goodfire Silico takes a vision transformer (ViT), loads ImageNet images, and for each head, visualizes the 196x196 matrix by creating animated arcs across patches. Haven't found another model or product that can do this level of comprehensive AI research, much less oneshot it.

Deedy

74,579 次观看 • 1 个月前

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ViPE: Video Pose Engine for 3D Geometric Perception Contributions: • A robust and efficient framework, ViPE, for estimating camera parameters and dense depth from diverse, in-the-wild videos. • A system design that integrates the strengths of classical SLAM (efficiency, scalability) and learned models (robustness), with key improvements in efficiency, dynamic object handling, and depth quality over prior work. • A large-scale dataset of annotated videos, created using ViPE, to facilitate future research in 3D computer vision.

MrNeRF

42,644 次观看 • 1 年前

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Felix Heide

27,858 次观看 • 1 年前

Scaling 3D scene data is a long-standing challenge in scene understanding, spatial reasoning, and robotics. Since scanning, reconstruction, and labeling are so labor-intensive, data scarcity has remained a major bottleneck. 🛑 To solve this, we propose SceneVerse++: Lifting Unlabeled Internet-level Data for 3D Scene Understanding (CVPR 2026). By reconstructing internet videos and annotating 3D scenes automatically, we’ve created a massive real-world dataset for end-to-end understanding. 🌐📐 SceneVerse++ makes it easy to scale "in-the-wild" 3D scenes toward more capable spatial reasoning systems. This significantly promotes progress in 3D VQA, visual navigation, and broader tasks in Embodied AI and Robotics. 🤖🦾 We are fully open-sourced! Check out the paper, code, and data here: 🌐 Project: 📄 Paper: 📊 Dataset: Code:

Scaling 3D scene data is a long-standing challenge in scene understanding, spatial reasoning, and robotics. Since scanning, reconstruction, and labeling are so labor-intensive, data scarcity has remained a major bottleneck. 🛑 To solve this, we propose SceneVerse++: Lifting Unlabeled Internet-level Data for 3D Scene Understanding (CVPR 2026). By reconstructing internet videos and annotating 3D scenes automatically, we’ve created a massive real-world dataset for end-to-end understanding. 🌐📐 SceneVerse++ makes it easy to scale "in-the-wild" 3D scenes toward more capable spatial reasoning systems. This significantly promotes progress in 3D VQA, visual navigation, and broader tasks in Embodied AI and Robotics. 🤖🦾 We are fully open-sourced! Check out the paper, code, and data here: 🌐 Project: 📄 Paper: 📊 Dataset: Code:

Siyuan Huang

12,831 次观看 • 4 个月前

There's a certain kind of energy that only a room full of believers can create. The Avaada Electro Townhall had exactly that. Across functions, across roles, the team came together to align on the vision, celebrate the journey so far, and look ahead at everything we're building. The activities, the conversations, the spirit in that room were entirely on another level. This is what growth looks like from the inside. Avaada Electro

There's a certain kind of energy that only a room full of believers can create. The Avaada Electro Townhall had exactly that. Across functions, across roles, the team came together to align on the vision, celebrate the journey so far, and look ahead at everything we're building. The activities, the conversations, the spirit in that room were entirely on another level. This is what growth looks like from the inside. Avaada Electro

Vineet Mittal

1,075,617 次观看 • 3 个月前

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

Sherwin Bahmani

66,865 次观看 • 11 个月前

GPT-6 Astra can already plan, code, and build. The more interesting question is what happens when it can call a dedicated 3D tool at the exact moment a project needs an asset. So we connected Hyper3D Rodin MCP and asked Astra to build an interactive Solar System Explorer. Astra handled the app logic and integration. When it needed 3D models, it called Hyper3D Rodin directly. Hyper3D Rodin generated the required assets, and Astra brought them back into the project and continued building from there. That is where the workflow gets useful. Instead of stopping to search for assets or creating them manually, Astra can keep moving while Hyper3D handles the specialized 3D generation, with more control over the output and assets that are easier to integrate into the final project. The result is a browser-based solar system where the generated 3D models are part of the actual experience, not just outputs sitting in a model viewer.

GPT-6 Astra can already plan, code, and build. The more interesting question is what happens when it can call a dedicated 3D tool at the exact moment a project needs an asset. So we connected Hyper3D Rodin MCP and asked Astra to build an interactive Solar System Explorer. Astra handled the app logic and integration. When it needed 3D models, it called Hyper3D Rodin directly. Hyper3D Rodin generated the required assets, and Astra brought them back into the project and continued building from there. That is where the workflow gets useful. Instead of stopping to search for assets or creating them manually, Astra can keep moving while Hyper3D handles the specialized 3D generation, with more control over the output and assets that are easier to integrate into the final project. The result is a browser-based solar system where the generated 3D models are part of the actual experience, not just outputs sitting in a model viewer.

GitHub Projects Community

11,097 次观看 • 8 天前

This AI paper just solved Google Earth's biggest problem. Satellites look down. Humans look across. That perspective gap is why 3D maps are limited to cities you can blanket with aerial flyovers. Skyfall-GS bridges the gap by synthesizing the views we never captured - rebuilding missing facades and street-level geometry using the world knowledge of diffusion models. And it does so with remarkable consistency. This is a taste of how we go from 3D maps in 3,000 cities to the entire planet. Watch my full video for the rundown:

This AI paper just solved Google Earth's biggest problem. Satellites look down. Humans look across. That perspective gap is why 3D maps are limited to cities you can blanket with aerial flyovers. Skyfall-GS bridges the gap by synthesizing the views we never captured - rebuilding missing facades and street-level geometry using the world knowledge of diffusion models. And it does so with remarkable consistency. This is a taste of how we go from 3D maps in 3,000 cities to the entire planet. Watch my full video for the rundown:

Bilawal Sidhu

135,003 次观看 • 10 个月前

A small clip from the first test of my AI glasses with a visually impaired person I’m grateful to everyone who has supported me so far. But my dream and vision go far beyond this i want to use technology to make a real difference in people’s lives and help our society I believe this project deserves to reach the world and connect with technology companies and organizations that can support us in taking it to full production

A small clip from the first test of my AI glasses with a visually impaired person I’m grateful to everyone who has supported me so far. But my dream and vision go far beyond this i want to use technology to make a real difference in people’s lives and help our society I believe this project deserves to reach the world and connect with technology companies and organizations that can support us in taking it to full production

khalifa aminu

280,776 次观看 • 27 天前

THIS GUY VIBE CODED A TRUE 3D PROPERTY TOUR TOOL WITH CLAUDE CODE not the fake 360 photo stitching that matterport does, actual 3D right now if you're a real estate agent and want a property tour you need: > a matterport camera ($3000+) > a photographer who knows how to use it > matterport's monthly subscription > and you still get a glorified panorama, not real 3D this guy built a tool that takes your existing captures (camera files, drone shots, 360 photos, EVEN phone captures) and turns them into a real interactive 3D tour in one workflow no specialized hardware, no photographer, no switching between 5 different tools real estate agents, property managers, airbnb hosts, architects, interior designers all of them are paying hundreds per property for something this tool does from captures they already have the painful part was never understanding the end result. everyone knows what a 3D tour looks like. the pain is going from a pile of messy capture files to something usable without it turning into a technical project this solves that with one workflow you dump your files in and get a 3D tour out insane what you can do with vibe coding

THIS GUY VIBE CODED A TRUE 3D PROPERTY TOUR TOOL WITH CLAUDE CODE not the fake 360 photo stitching that matterport does, actual 3D right now if you're a real estate agent and want a property tour you need: > a matterport camera ($3000+) > a photographer who knows how to use it > matterport's monthly subscription > and you still get a glorified panorama, not real 3D this guy built a tool that takes your existing captures (camera files, drone shots, 360 photos, EVEN phone captures) and turns them into a real interactive 3D tour in one workflow no specialized hardware, no photographer, no switching between 5 different tools real estate agents, property managers, airbnb hosts, architects, interior designers all of them are paying hundreds per property for something this tool does from captures they already have the painful part was never understanding the end result. everyone knows what a 3D tour looks like. the pain is going from a pile of messy capture files to something usable without it turning into a technical project this solves that with one workflow you dump your files in and get a 3D tour out insane what you can do with vibe coding

Om Patel

158,397 次观看 • 4 个月前

Infinite Photorealistic Worlds using Procedural Generation paper page: introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. Infinigen offers broad coverage of objects and scenes in the natural world including plants, animals, terrains, and natural phenomena such as fire, cloud, rain, and snow. Infinigen can be used to generate unlimited, diverse training data for a wide range of computer vision tasks including object detection, semantic segmentation, optical flow, and 3D reconstruction. We expect Infinigen to be a useful resource for computer vision research and beyond.

Infinite Photorealistic Worlds using Procedural Generation paper page: introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. Infinigen offers broad coverage of objects and scenes in the natural world including plants, animals, terrains, and natural phenomena such as fire, cloud, rain, and snow. Infinigen can be used to generate unlimited, diverse training data for a wide range of computer vision tasks including object detection, semantic segmentation, optical flow, and 3D reconstruction. We expect Infinigen to be a useful resource for computer vision research and beyond.

AK

275,530 次观看 • 3 年前

Exciting news! Our MedSAM is featured in Computer Vision News! 🎉🎉🎉🙌 Check it out: We are not stopping at papers and aiming to take integration to the next level by incorporating MedSAM with 3D Slicer, a top-tier medical imaging viewer. 🏥 **Segment Anything in Medical Images!** Read the paper here: Get your hands on the code: Explore MedSAM@3Dslice: Huge shoutout to the lead contributor, Jun Ma (JunMa), and a big thank you to Reza Asakereh for his invaluable contributions to MedSAMSlicer! 👏 #MedSAM #MedicalImaging

Exciting news! Our MedSAM is featured in Computer Vision News! 🎉🎉🎉🙌 Check it out: We are not stopping at papers and aiming to take integration to the next level by incorporating MedSAM with 3D Slicer, a top-tier medical imaging viewer. 🏥 Segment Anything in Medical Images! Read the paper here: Get your hands on the code: Explore MedSAM@3Dslice: Huge shoutout to the lead contributor, Jun Ma (JunMa), and a big thank you to Reza Asakereh for his invaluable contributions to MedSAMSlicer! 👏 #MedSAM #MedicalImaging

Bo Wang

19,527 次观看 • 2 年前

Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors paper page: Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors, these methods often lead to geometric anomalies and multi-view inconsistency. Recently, researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets, albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches, we propose Bidirectional Diffusion(BiDiff), a unified framework that incorporates both a 3D and a 2D diffusion process, to preserve both 3D fidelity and 2D texture richness, respectively. Moreover, as a simple combination may yield inconsistent generation results, we further bridge them with novel bidirectional guidance. In addition, our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization, reducing the generation process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality, diverse, and scalable 3D generation.

Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors paper page: Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors, these methods often lead to geometric anomalies and multi-view inconsistency. Recently, researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets, albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches, we propose Bidirectional Diffusion(BiDiff), a unified framework that incorporates both a 3D and a 2D diffusion process, to preserve both 3D fidelity and 2D texture richness, respectively. Moreover, as a simple combination may yield inconsistent generation results, we further bridge them with novel bidirectional guidance. In addition, our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization, reducing the generation process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality, diverse, and scalable 3D generation.

AK

38,853 次观看 • 2 年前