6 papers
CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos
Chengfeng Zhao, Jiazhi Shu, Yubo Zhao +7
In this paper, we find that the generation of 3D human motions and 2D human videos is intrinsically coupled. 3D motions provide the structural prior for plausibility and consistenc…
RecGen3D: Reconstruction-Guided 3D Generation in a Shared Canonical Space
Zhisheng Huang, Jiahao Chen, Cheng Lin +10
Sparse-view 3D modeling represents a fundamental tension between reconstruction fidelity and generative plausibility. While feed-forward reconstruction excels in efficiency and inp…
GO-Renderer: Generative Object Rendering with 3D-aware Controllable Video Diffusion Models
Zekai Gu, Shuoxuan Feng, Yansong Wang +6
Reconstructing a renderable 3D model from images is a useful but challenging task. Recent feedforward 3D reconstruction methods have demonstrated remarkable success in efficiently…
FlexAM: Flexible Appearance-Motion Decomposition for Versatile Video Generation Control
Mingzhi Sheng, Zekai Gu, Peng Li +4
Effective and generalizable control in video generation remains a significant challenge. While many methods rely on ambiguous or task-specific signals, we argue that a fundamental…
RefAny3D: 3D Asset-Referenced Diffusion Models for Image Generation
Hanzhuo Huang, Qingyang Bao, Zekai Gu +4
In this paper, we propose a 3D asset-referenced diffusion model for image generation, exploring how to integrate 3D assets into image diffusion models. Existing reference-based ima…
Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control
Zekai Gu, Rui Yan, Jiahao Lu +9
Diffusion models have demonstrated impressive performance in generating high-quality videos from text prompts or images. However, precise control over the video generation process,…