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📢📢📢 Excited to share our new work Autonomous Character-Scene Interaction Synthesis from Text Instruction (Siggraph Asia 24). It presents a unified model for flexible scene-conditioned motion generation given text, scene, trajectory conditions. The results with smooth interaction look very impressive! 📰Paper: Project: Code and data will be released soon.

Siyuan Huang

5,096 subscribers

11,451 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von Siyuan Huang

Siyuan Huangvor 1 Jahr

Some details and designs for our work: (1/5) We tackle the exciting challenge of generating scene-aware interaction motions for virtual characters based on text instructions and target locations within a 3D environment. There are some beautiful results showing how the generated motion interacts with the 3D scenes instructed by input text.

Profilbild von Siyuan Huang

Siyuan Huangvor 1 Jahr

（2/5) Our motion generation method handles both locomotion and interaction motions. We leverage an auto-regressive conditional diffusion model that takes language guidance, the goal location for the current segment, and the scene voxel as input.

Profilbild von Siyuan Huang

Siyuan Huangvor 1 Jahr

(3/5) The character's scene awareness comes from a local occupancy grid. Each voxel in the grid indicates whether the corresponding location is occupied by a scene object. Such representation enhances the understanding of 3D space and interaction.

Profilbild von Siyuan Huang

Siyuan Huangvor 1 Jahr

(4/5) Given the same trajectory and scene, our model generates characters that actively avoid penetrating the scene and exhibit natural cues of scene awareness.

Profilbild von Siyuan Huang

Siyuan Huangvor 1 Jahr

(5/5) Our model is supercharged by LINGO, a comprehensive motion-captured dataset. We employ a synthetic vision approach, where scene objects are projected into the virtual view displayed in a VR headset worn by the motion actor.

Profilbild von one واحد

one واحدvor 1 Jahr

nice work 👏

Profilbild von Sentients

Sentientsvor 1 Jahr

🔥🔥🔥🔥

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21,908 Aufrufe • vor 1 Jahr

Excited to share our latest work on 🎧spatial audio-driven human motion generation. We aim to tackle a largely underexplored yet important problem of enabling virtual humans to move naturally in response to spatial audio—capturing not just what is heard, but also where the sound is coming from. To this end, we introduce the Spatial Audio-Driven Human Motion (SAM) dataset—the first comprehensive dataset featuring paired high-quality human motion and spatial audio recordings. For benchmarking, we develop a generative framework for human MOtion generation driven by SPAtial audio, termed MOSPA, which learns to synthesize realistic and diverse human motions conditioned on spatial audio input. We hope this research could provide a foundation for future research in spatial perception, virtual characters, and embodied AI. The dataset and model will be open-sourced soon. A big thank you to our intern, Shuyang Xu, for the wonderful collaboration! Congratulations, Shuyang! Project page: Paper: Video: #Animation #CG #CV #AIGC #DL #Deeplearning #Motion #Graphics #AI #GenerativeAI

Excited to share our latest work on 🎧spatial audio-driven human motion generation. We aim to tackle a largely underexplored yet important problem of enabling virtual humans to move naturally in response to spatial audio—capturing not just what is heard, but also where the sound is coming from. To this end, we introduce the Spatial Audio-Driven Human Motion (SAM) dataset—the first comprehensive dataset featuring paired high-quality human motion and spatial audio recordings. For benchmarking, we develop a generative framework for human MOtion generation driven by SPAtial audio, termed MOSPA, which learns to synthesize realistic and diverse human motions conditioned on spatial audio input. We hope this research could provide a foundation for future research in spatial perception, virtual characters, and embodied AI. The dataset and model will be open-sourced soon. A big thank you to our intern, Shuyang Xu, for the wonderful collaboration! Congratulations, Shuyang! Project page: Paper: Video: #Animation #CG #CV #AIGC #DL #Deeplearning #Motion #Graphics #AI #GenerativeAI

Zhiyang (Frank) Dou

14,610 Aufrufe • vor 1 Jahr

📢 Eyeline and Netflix latest research, 🌟Go-with-the-Track🌟, has been accepted to #SIGGRAPH2026. We propose a video generation framework that jointly conditions on multiple reference images and reference-anchored point tracks, enabling a range of content creation applications within a unified model: • Motion-preserving video restylization using multiple reference images; • Mesh- or keypoint-driven compositing and stylization; • Camera motion control for both static and dynamic scenes using multiple reference images captured from different view-points and time steps. ✊Congrats to the team: Koichi Namekata, Yash Kant, Zhizheng Liu, Ryan Burgert, Yuancheng Xu, Jordan Lin, Emmett Steven, Julien Philip, LiMa, Andrea Vedaldi, Paul Debevec, Ning Yu 📰 Paper: 🌐 Project: ⌨️ Code: *** This work is part of the ongoing research and development at Eyeline and Netflix, and we look forward to seeing its techniques and workflows adopted in future productions.

📢 Eyeline and Netflix latest research, 🌟Go-with-the-Track🌟, has been accepted to #SIGGRAPH2026. We propose a video generation framework that jointly conditions on multiple reference images and reference-anchored point tracks, enabling a range of content creation applications within a unified model: • Motion-preserving video restylization using multiple reference images; • Mesh- or keypoint-driven compositing and stylization; • Camera motion control for both static and dynamic scenes using multiple reference images captured from different view-points and time steps. ✊Congrats to the team: Koichi Namekata, Yash Kant, Zhizheng Liu, Ryan Burgert, Yuancheng Xu, Jordan Lin, Emmett Steven, Julien Philip, LiMa, Andrea Vedaldi, Paul Debevec, Ning Yu 📰 Paper: 🌐 Project: ⌨️ Code: *** This work is part of the ongoing research and development at Eyeline and Netflix, and we look forward to seeing its techniques and workflows adopted in future productions.

Ning Yu

28,392 Aufrufe • vor 1 Monat

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

83,657 Aufrufe • vor 3 Jahren

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,257 Aufrufe • vor 11 Monaten