activity
20232026
collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV2026

Spatiotemporally Decoupled Autoregressive Diffusion Model for Human Motion Generation

Chengqun Yang, Liang Xu, Yanping Li +4

Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For representation, Vector Quantizat…

cs.CV2026

Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis

Liang Xu, Chengqun Yang, Zili Lin +6

The capability to perceive and synthesize human-human interactions is fundamental to developing intelligent digital human systems. However, existing datasets and modeling approache…

cs.CV2026

MRBench: A Comprehensive Benchmark for Human Motion-Text Retrieval

Fulong Liu, Liang Xu, Chengqun Yang +3

Human motion-text retrieval provides a rigorous means of assessing cross-modal alignment. Prevailing benchmarks are dominated by homogeneous indoor motions, imbalanced motion distr…

cs.CV2025

Perceiving and Acting in First-Person: A Dataset and Benchmark for Egocentric Human-Object-Human Interactions

Liang Xu, Chengqun Yang, Zili Lin +11

Learning action models from real-world human-centric interaction datasets is important towards building general-purpose intelligent assistants with efficiency. However, most existi…

cs.CV2024

MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotations

Liang Xu, Shaoyang Hua, Zili Lin +6

In this paper, we tackle the problem of how to build and benchmark a large motion model (LMM). The ultimate goal of LMM is to serve as a foundation model for versatile motion-relat…

cs.CV2024

HIMO: A New Benchmark for Full-Body Human Interacting with Multiple Objects

Xintao Lv, Liang Xu, Yichao Yan +8

Generating human-object interactions (HOIs) is critical with the tremendous advances of digital avatars. Existing datasets are typically limited to humans interacting with a single…

cs.CV2024

ReGenNet: Towards Human Action-Reaction Synthesis

Liang Xu, Yizhou Zhou, Yichao Yan +5

Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes…

cs.CV2023

Inter-X: Towards Versatile Human-Human Interaction Analysis

Liang Xu, Xintao Lv, Yichao Yan +10

The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccu…