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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 просмотров • 2 лет назад •via X (Twitter)

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Фото профиля Siyuan Huang

Siyuan Huang2 лет назад

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.

Фото профиля Siyuan Huang

Siyuan Huang2 лет назад

（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.

Фото профиля Siyuan Huang

Siyuan Huang2 лет назад

(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.

Фото профиля Siyuan Huang

Siyuan Huang2 лет назад

(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.

Фото профиля Siyuan Huang

Siyuan Huang2 лет назад

(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.

Фото профиля one واحد

one واحد2 лет назад

nice work 👏

Фото профиля Sentients

Sentients2 лет назад

🔥🔥🔥🔥

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17,503 просмотров • 3 лет назад

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22,371 просмотров • 5 месяцев назад

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Hedra

180,676 просмотров • 1 год назад

📢 IntrinsiX: High-Quality PBR Generation using Image Priors 📢 From text input, we generate renderable PBR maps! Next to editable image generation, our predictions can be distilled into room-scale scenes using SDS for large-scale PBR texture generation. We first train separate LoRA modules for the intrinsic properties of albedo, rough/metal, normal. Then, we introduce cross-intrinsic attention using a rerendering loss with importance-weighted light sampling to enable coherent PBR generation. Our method outperforms text -> image -> PBR methods both in generalization and quality, since directly generating PBR maps does not suffer from the inherent ambiguity of intrinsic image decomposition. In addition, our design choice facilitates SDS-based PBR texture distillation. 🌍 🎥 Great work by Peter Kocsis, Lukas Höllein

📢 IntrinsiX: High-Quality PBR Generation using Image Priors 📢 From text input, we generate renderable PBR maps! Next to editable image generation, our predictions can be distilled into room-scale scenes using SDS for large-scale PBR texture generation. We first train separate LoRA modules for the intrinsic properties of albedo, rough/metal, normal. Then, we introduce cross-intrinsic attention using a rerendering loss with importance-weighted light sampling to enable coherent PBR generation. Our method outperforms text -> image -> PBR methods both in generalization and quality, since directly generating PBR maps does not suffer from the inherent ambiguity of intrinsic image decomposition. In addition, our design choice facilitates SDS-based PBR texture distillation. 🌍 🎥 Great work by Peter Kocsis, Lukas Höllein

Matthias Niessner

21,921 просмотров • 1 год назад

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

14,704 просмотров • 1 год назад

📢 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

28,759 просмотров • 3 месяцев назад