2 citations · 2 across the 11 of their papers we have counts for
4 papers · 2 filters
Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data
Ke Fan, Shunlin Lu, Minyue Dai +6
Generating diverse and natural human motion sequences based on textual descriptions constitutes a fundamental and challenging research area within the domains of computer vision, g…
ARMO: Autoregressive Rigging for Multi-Category Objects
Mingze Sun, Shiwei Mao, Keyi Chen +5
Recent advancements in large-scale generative models have significantly improved the quality and diversity of 3D shape generation. However, most existing methods focus primarily on…
Towards Synthesized and Editable Motion In-Betweening Through Part-Wise Phase Representation
Minyue Dai, Ke Fan, Bin Ji +5
Styled motion in-betweening is crucial for computer animation and gaming. However, existing methods typically encode motion styles by modeling whole-body motions, often overlooking…
GAS: Generative Avatar Synthesis from a Single Image
Yixing Lu, Junting Dong, Youngjoong Kwon +3
We present a unified and generalizable framework for synthesizing view-consistent and temporally coherent avatars from a single image, addressing the challenging task of single-ima…