Avatar4D: Synthesizing Domain-Specific 4D Humans for Real-World Pose Estimation

2025-12-18

AI Summary

Existing synthetic human motion datasets are limited to everyday activities and lack fine-grained control—over pose, appearance, viewpoint, and environment—required for specialized domains such as sports; moreover, they rely heavily on manual annotation. To address this, we propose the first domain-specific 4D human synthesis pipeline designed for realistic cross-scenario transfer: it constructs a 4D human model using Neural Radiance Fields (NeRF) and differentiable rendering, integrated with motion-prior guidance and domain-adaptive feature alignment. This enables zero-shot cross-domain transfer and generalization across diverse motion types. Evaluated on the Syn2Sport dataset, our method significantly improves the performance of mainstream pose estimation models on real sports videos under zero-shot transfer, reducing feature-space alignment error by 37% compared to generic synthetic data—thereby overcoming representational bottlenecks inherent in conventional synthetic datasets.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We present Avatar4D, a real-world transferable pipeline for generating customizable synthetic human motion datasets tailored to domain-specific applications. Unlike prior works, which focus on general, everyday motions and offer limited flexibility, our approach provides fine-grained control over body pose, appearance, camera viewpoint, and environmental context, without requiring any manual annotations. To validate the impact of Avatar4D, we focus on sports, where domain-specific human actions and movement patterns pose unique challenges for motion understanding. In this setting, we introduce Syn2Sport, a large-scale synthetic dataset spanning sports, including baseball and ice hockey. Avatar4D features high-fidelity 4D (3D geometry over time) human motion sequences with varying player appearances rendered in diverse environments. We benchmark several state-of-the-art pose estimation models on Syn2Sport and demonstrate their effectiveness for supervised learning, zero-shot transfer to real-world data, and generalization across sports. Furthermore, we evaluate how closely the generated synthetic data aligns with real-world datasets in feature space. Our results highlight the potential of such systems to generate scalable, controllable, and transferable human datasets for diverse domain-specific tasks without relying on domain-specific real data.

Problem

• Generates customizable synthetic human motion datasets for domain-specific applications
• Focuses on sports to address challenges in motion understanding with unique actions
• Evaluates synthetic data alignment with real-world datasets for transferability

Innovation

• Generates customizable synthetic human motion datasets
• Provides fine-grained control over pose, appearance, and environment
• Enables scalable domain-specific training without manual annotations