1 citations · 2 across the 22 of their papers we have counts for
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HaPRL: Human-Anchored Process Reinforcement Learning for Visual Search Agent
Zhangquan Chen, Yaoxin Niu, Xiang An +5
Multi-turn visual search agents answer questions about high-resolution images by iteratively deciding where to look. Reinforcement learning for these agents rewards only the final…
EviViT: Evidence-Adaptive Vision Transformers for Fine-Grained Perception
Yaoxin Niu, Zhangquan Chen, Yang Zhang +5
Fine-grained visual perception enables vision-language models to distinguish subtle attributes and ground their answers in visual evidence. In high-resolution scenes, processing th…
Spatial-OPSD: Self-Improving Spatial Reasoning via Label-Free Self-Distillation
Zhenyu Liu, Zhangquan Chen, Keyi Chen +4
Vision-language models (VLMs) increasingly operate in embodied and spatially grounded settings, where accurate understanding of depth, viewpoint, and three-dimensional relations is…
Video Models as Native 4D Renderers: World-Grounded Conditioning from Animated Mesh
Junhao Chen, Mingjin Chen, Henghaofan Zhang +9
Pretrained video diffusion models can act as renderers when the desired scene state is already specified by an animated mesh, a camera trajectory, and a reference image. This 4D ge…
Bunraku: Turning a Single Illustration into an Editable Live2D Character
Junhao Chen, Jingjia Mao, Dayong Li +6
Live2D is the dominant 2D character-animation format for anime characters and virtual avatars, representing each character as a stack of RGBA layers driven by per-layer mesh deform…
Engine-Native Editable 3D World Reconstruction with Objects and Lighting
Junhao Chen, Xinghao Chen, Henghaofan Zhang +8
Editable 3D scene creation requires object instances and lights that can be inspected, moved, and imported into standard engines, yet existing single-image methods largely stop at…
One Video, One World: Turning Monocular Video into Physical 4D Scenes
Junhao Chen, Boran Zhang, Mingjin Chen +7
We introduce \textbf{OVOW}, the first training-free system that reconstructs \emph{instance-level, simulation-ready} 4D mesh scenes from a single monocular video. Recent 4D reconst…
4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +9
Dynamic spatial reasoning from monocular video is essential for bridging visual intelligence and the physical world, yet remains challenging for vision-language models (VLMs). Prio…
LottieGPT: Tokenizing Vector Animation for Autoregressive Generation
Junhao Chen, Kejun Gao, Yuehan Cui +8
Despite rapid progress in video generation, existing models are incapable of producing vector animation, a dominant and highly expressive form of multimedia on the Internet. Vector…
Topology-Agnostic Animal Motion Generation from Text Prompt
Keyi Chen, Mingze Sun, Zhenyu Liu +2
Motion generation is fundamental to computer animation and widely used across entertainment, robotics, and virtual environments. While recent methods achieve impressive results, mo…
Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +8
Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limit…
SIFThinker: Spatially-Aware Image Focus for Visual Reasoning
Zhangquan Chen, Ruihui Zhao, Chuwei Luo +4
Current multimodal large language models (MLLMs) still face significant challenges in complex visual tasks (e.g., spatial understanding, fine-grained perception). Prior methods hav…
DanceTogether! Identity-Preserving Multi-Person Interactive Video Generation
Junhao Chen, Mingjin Chen, Jianjin Xu +9
Controllable video generation (CVG) has advanced rapidly, yet current systems falter when more than one actor must move, interact, and exchange positions under noisy control signal…
NFR: Neural Feature-Guided Non-Rigid Shape Registration
Zhangquan Chen, Puhua Jiang, Mingze Sun +1
In this paper, we propose a novel learning-based framework for 3D shape registration, which overcomes the challenges of significant non-rigid deformation and partiality undergoing…
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…
DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters
Mingze Sun, Junhao Chen, Junting Dong +7
Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging tas…
SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation
Mingze Sun, Chen Guo, Puhua Jiang +3
In this paper, we propose SRIF, a novel Semantic shape Registration framework based on diffusion-based Image morphing and Flow estimation. More concretely, given a pair of extrinsi…
Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony
Chao Xu, Mingze Sun, Zhi-Qi Cheng +5
In this paper, we propose a novel framework, Combo, for harmonious co-speech holistic 3D human motion generation and efficient customizable adaption. In particular, we identify tha…
DV-Matcher: Deformation-based Non-Rigid Point Cloud Matching Guided by Pre-trained Visual Features
Zhangquan Chen, Puhua Jiang, Ruqi Huang
In this paper, we present DV-Matcher, a novel learning-based framework for estimating dense correspondences between non-rigidly deformable point clouds. Learning directly from unst…
Beyond Talking -- Generating Holistic 3D Human Dyadic Motion for Communication
Mingze Sun, Chao Xu, Xinyu Jiang +3
In this paper, we introduce an innovative task focused on human communication, aiming to generate 3D holistic human motions for both speakers and listeners. Central to our approach…
Non-Rigid Shape Registration via Deep Functional Maps Prior
Puhua Jiang, Mingze Sun, Ruqi Huang
In this paper, we propose a learning-based framework for non-rigid shape registration without correspondence supervision. Traditional shape registration techniques typically rely o…