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Supervised learning has held 3D Vision back for too long. Meet RayZer — a self-supervised 3D model trained with zero 3D labels: ❌ No supervision of camera & geometry ✅ Just RGB images And the wild part? RayZer outperforms supervised methods (as 3D labels from COLMAP is noisy) 🌐... show more

Hanwen Jiang

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70,830 görüntüleme • 1 yıl önce •via X (Twitter)

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Hanwen Jiang profil fotoğrafı

Hanwen Jiang1 yıl önce

🔍 How does RayZer work? It performs 3D-aware image auto-encoding, which first disentangles images into scene + camera (reconstruction), then re-entangles them back into images (rendering) and learn via RGB loss. The key is splitting the images into two sets — one set to reconstruct scene, and the other to provide supervision, which avoids trivial non-3D solutions.

Hanwen Jiang profil fotoğrafı

Hanwen Jiang1 yıl önce

🤯 RayZer outperforms supervised methods — why? Turns out, 3D labels from COLMAP are noisy. GS-LRM and LVSM consistently fail on scenes of glasses, high luminance intensity, and white walls. These are cases where COLMAP usually fail. This highlights the need for self-supervised learning — and shows just how powerful it can be.

Hanwen Jiang profil fotoğrafı

Hanwen Jiang1 yıl önce

RayZer is similar to video generation models philosophically: ❌ No 3D-aware architecture ❌ No 3D representation & rendering equation ❌ No 3D supervision ✅ But 3D awareness emerges. (We show more inference results)

Hanwen Jiang profil fotoğrafı

Hanwen Jiang1 yıl önce

Joint work with @HaoTan5 @totoro97_ @Haian_Jin @__yuezhao__ @Sai__Bi @KaiZhang9546 @fujun_luan Kalyan Sunkavalli @qixing_huang @geopavlakos

Dmytro Mishkin 🇺🇦 profil fotoğrafı

Dmytro Mishkin 🇺🇦1 yıl önce

Amazing! Dare to try it in Image Matching Challenge? :)

Hanwen Jiang profil fotoğrafı

Hanwen Jiang1 yıl önce

haha, I don't think it works on images with different lighting conditions now

relu profil fotoğrafı

relu1 yıl önce

Super cool. I’ve been looking for pose estimation without any supervision from SfM and couldn’t find any papers! Was super surprised. I’m glad someone finally got this working

Jeffrey Ouyang-Zhang profil fotoğrafı

Jeffrey Ouyang-Zhang1 yıl önce

cool work!

Jang Hyun (Vincent) Cho profil fotoğrafı

Jang Hyun (Vincent) Cho1 yıl önce

amazing

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2D is what the camera sees.👀 3D is what the world is. 🌍 Today, we’re proudly open-sourcing QuerySplat — an open-source feed-forward 3D model that turns a few unposed images into a navigable 3D scene in seconds (run on 4090🪶). Your input: a few unposed images Our output: a sharp, navigable 3D scene — generated in seconds! 🚀 🧠 organize scenes with 3D queries, not pixel-bound Gaussians 📷 no camera poses or manual calibration required ⚡ reconstruct new scenes in a single forward pass ✨ preserve sharp geometry and high-frequency appearance 🏆 achieve state-of-the-art results on DL3DV-Evaluation 🔓 code and model weights are now open-source From pixels to scenes, we are making 3D generation scalable. Try our free iOS app — it’s a lot of fun! 😆 🔗 Try it on the Web: 🔗 Paper: 🔗 Code & weights: 🔗 Project page: 🔗 #AIGC #3DGS #AI #Computervision #3D #artist #3dartist #3d

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18,899 görüntüleme • 10 ay önce

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📢Happy to present Convex Splatting, a novel way for 3D reconstruction based on 3D smooth convexes. For the first time, a splatting-based method reaches the quality of NeRF sota methods but with real-time rendering and few primitives!! I expect this to replace Gaussian splatting for 3D in the coming months. CODE RELEASED TODAY! joint work with collaborators from Université de Liège Visual Geometry Group (VGG) , KAUST Computer Vision Lab (IVUL) a thread 🧵 1/n

Abdullah Hamdi

71,661 görüntüleme • 1 yıl önce

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

305,704 görüntüleme • 3 yıl önce

BREAKING: Tesla's FSD (Supervised) is officially launching today to the general public across Australia and New Zealand. All HW4 Model 3s and Model Ys now have access. Launch details: • Eligible customers will be able to upgrade their vehicle to enable FSD (Supervised) by way of an ‘over-the-air’ update from anywhere with a WiFi connection. • FSD (Supervised) is available as an optional package for every new Tesla vehicle purchased in Australia and New Zealand. • Customers who have already taken delivery of an eligible vehicle without purchasing Full Self-Driving Capability can upgrade by purchasing the FSD (Supervised) package. Tesla is also looking to make FSD (Supervised) available soon by way of a subscription model for eligible vehicles for $149/month (AUD). No timeline for HW3 owners yet. Source:

BREAKING: Tesla's FSD (Supervised) is officially launching today to the general public across Australia and New Zealand. All HW4 Model 3s and Model Ys now have access. Launch details: • Eligible customers will be able to upgrade their vehicle to enable FSD (Supervised) by way of an ‘over-the-air’ update from anywhere with a WiFi connection. • FSD (Supervised) is available as an optional package for every new Tesla vehicle purchased in Australia and New Zealand. • Customers who have already taken delivery of an eligible vehicle without purchasing Full Self-Driving Capability can upgrade by purchasing the FSD (Supervised) package. Tesla is also looking to make FSD (Supervised) available soon by way of a subscription model for eligible vehicles for $149/month (AUD). No timeline for HW3 owners yet. Source:

Sawyer Merritt

342,041 görüntüleme • 11 ay önce

📢 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

66,831 görüntüleme • 11 ay önce

📢📢 𝐀𝐯𝐚𝐭𝟑𝐫 📢📢 Avat3r creates high-quality 3D head avatars from just a few input images in a single forward pass with a new dynamic 3DGS reconstruction model. Video: Project: Our core idea is to make Gaussian Reconstruction Models animatable. We find that a simple cross-attention to an expression code sequence is already sufficient to model complex facial expressions. We then incorporate position maps from DUSt3R and feature maps from Sapiens to facilitate the prediction task. While DUSt3R's position maps act as a pixel-aligned initialization for the Gaussians' positions, the Sapiens feature maps help the cross-view transformer to match corresponding image tokens in the 4 input images. One major challenge in creating a 3D head avatar from smartphone images comes from inconsistent facial expressions when the subject could not remain perfectly static during the capture. We eliminate this static requirement by simply showing our model input images with different facial expressions during training. This technique makes our model robust to inconsistent input images later on. Finally, we show that despite the model has been trained with 4 input images, one can even create a 3D head avatar when only a single image is available. To achieve this, we employ a pre-trained 3D GAN to lift the single image to 3D and then render the 4 input images for our model. This allows us to create 3D head avatars from single images and even highly out-of-distribution examples like AI generated faces, paintings or statues. Great work by Tobias Kirschstein from his internship at Meta with Javier Romero, Artem Sevastopolsky, and Shunsuke Saito

Matthias Niessner

74,810 görüntüleme • 1 yıl önce