5 papers
RealisMotion: Decomposed Human Motion Control and Video Generation in the World Space
Jingyun Liang, Jingkai Zhou, Shikai Li +5
Generating human videos with realistic and controllable motions is a challenging task. While existing methods can generate visually compelling videos, they lack separate control ov…
EarthCrafter: Scalable 3D Earth Generation via Dual-Sparse Latent Diffusion
Shang Liu, Chenjie Cao, Chaohui Yu +3
Despite the remarkable developments achieved by recent 3D generation works, scaling these methods to geographic extents, such as modeling thousands of square kilometers of Earth's…
LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion
Yisu Zhang, Chenjie Cao, Chaohui Yu +1
Video Diffusion Models (VDMs) have demonstrated remarkable capabilities in synthesizing realistic videos by learning from large-scale data. Although vanilla Low-Rank Adaptation (Lo…
Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generation
Chenjie Cao, Jingkai Zhou, Shikai Li +5
Camera and human motion controls have been extensively studied for video generation, but existing approaches typically address them separately, suffering from limited data with hig…
MVGenMaster: Scaling Multi-View Generation from Any Image via 3D Priors Enhanced Diffusion Model
Chenjie Cao, Chaohui Yu, Shang Liu +3
We introduce MVGenMaster, a multi-view diffusion model enhanced with 3D priors to address versatile Novel View Synthesis (NVS) tasks. MVGenMaster leverages 3D priors that are warpe…