4 citations · 9 across the 7 of their papers we have counts for
9 papers
RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing
Tianyuan Qu, Lei Ke, Xiaohang Zhan +6
Instruction-based image editing enables natural-language control over visual modifications, yet existing models falter under Instruction-Visual Complexity (IV-Complexity), where in…
Social Agent: Mastering Dyadic Nonverbal Behavior Generation via Conversational LLM Agents
Zeyi Zhang, Yanju Zhou, Heyuan Yao +3
We present Social Agent, a novel framework for synthesizing realistic and contextually appropriate co-speech nonverbal behaviors in dyadic conversations. In this framework, we deve…
LaRender: Training-Free Occlusion Control in Image Generation via Latent Rendering
Xiaohang Zhan, Dingming Liu
We propose a novel training-free image generation algorithm that precisely controls the occlusion relationships between objects in an image. Existing image generation methods typic…
DreamMapping: High-Fidelity Text-to-3D Generation via Variational Distribution Mapping
Zeyu Cai, Duotun Wang, Yixun Liang +4
Score Distillation Sampling (SDS) has emerged as a prevalent technique for text-to-3D generation, enabling 3D content creation by distilling view-dependent information from text-to…
Programmable Motion Generation for Open-Set Motion Control Tasks
Hanchao Liu, Xiaohang Zhan, Shaoli Huang +2
Character animation in real-world scenarios necessitates a variety of constraints, such as trajectories, key-frames, interactions, etc. Existing methodologies typically treat singl…
HeadEvolver: Text to Head Avatars via Expressive and Attribute-Preserving Mesh Deformation
Duotun Wang, Hengyu Meng, Zeyu Cai +6
Current text-to-avatar methods often rely on implicit representations (e.g., NeRF, SDF, and DMTet), leading to 3D content that artists cannot easily edit and animate in graphics so…
SemanticBoost: Elevating Motion Generation with Augmented Textual Cues
Xin He, Shaoli Huang, Xiaohang Zhan +2
Current techniques face difficulties in generating motions from intricate semantic descriptions, primarily due to insufficient semantic annotations in datasets and weak contextual…
HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting
Xian Liu, Xiaohang Zhan, Jiaxiang Tang +5
Realistic 3D human generation from text prompts is a desirable yet challenging task. Existing methods optimize 3D representations like mesh or neural fields via score distillation…
TapMo: Shape-aware Motion Generation of Skeleton-free Characters
Jiaxu Zhang, Shaoli Huang, Zhigang Tu +4
Previous motion generation methods are limited to the pre-rigged 3D human model, hindering their applications in the animation of various non-rigged characters. In this work, we pr…