arXiv:2501.09782v1 Announce Type: cross
Abstract: Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods focus on training innovative architectural designs on confined datasets. In this work, we investigate the impact of scaling up EHPS towards a family of generalist foundation models. 1) For data scaling, we perform a systematic investigation on 40 EHPS datasets, encompassing a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. Ultimately, we achieve diminishing returns at 10M training instances from diverse data sources. 2) For model scaling, we take advantage of vision transformers (up to ViT-Huge as the backbone) to study the scaling law of model sizes in EHPS. To exclude the influence of algorithmic design, we base our experiments on two minimalist architectures: SMPLer-X, which consists of an intermediate step for hand and face localization, and SMPLest-X, an even simpler version that reduces the network to its bare essentials and highlights significant advances in the capture of articulated hands. With big data and the large model, the foundation models exhibit strong performance across diverse test benchmarks and excellent transferability to even unseen environments. Moreover, our finetuning strategy turns the generalist into specialist models, allowing them to achieve further performance boosts. Notably, our foundation models consistently deliver state-of-the-art results on seven benchmarks such as AGORA, UBody, EgoBody, and our proposed SynHand dataset for comprehensive hand evaluation. (Code is available at: https://github.com/wqyin/SMPLest-X).
Expressive human pose and shape estimation (EHPS) is a fascinating field that involves capturing the movements and shapes of the human body, hands, and face. This technology has a wide range of applications, from animation and virtual reality to artificial reality and multimedia information systems.
In this article, the authors explore the potential of scaling up EHPS towards the development of generalist foundation models. Currently, state-of-the-art methods in EHPS are focused on training innovative architectural designs on specific datasets. However, this approach has limitations as a model trained on a single dataset may not be able to handle a wide range of scenarios.
To overcome this limitation, the authors perform a systematic investigation on 40 EHPS datasets, covering various scenarios. By analyzing and benchmarking these datasets, they optimize their training scheme and select datasets that lead to significant improvements in EHPS capabilities. The authors find that they achieve diminishing returns at around 10 million training instances, indicating the importance of diverse data sources.
In addition to data scaling, the authors also investigate model scaling using vision transformers as the backbone. By using minimalist architectures, they study the scaling law of model sizes in EHPS, excluding the influence of algorithmic design. They find that with big data and large models, the foundation models exhibit strong performance across diverse test benchmarks and can even transfer their knowledge to unseen environments.
Furthermore, the authors develop a finetuning strategy that turns the generalist foundation models into specialist models, allowing them to achieve further performance boosts. These foundation models consistently deliver state-of-the-art results on multiple benchmarks, including AGORA, UBody, EgoBody, and the authors’ proposed SynHand dataset for comprehensive hand evaluation. This highlights the effectiveness and versatility of the developed EHPS techniques.
The concepts explored in this article highlight the multi-disciplinary nature of EHPS. It involves aspects of computer vision, machine learning, artificial intelligence, animation, and virtual reality. The ability to accurately capture and estimate human pose and shape has tremendous potential in various fields, including entertainment, gaming, healthcare, and even robotics.
In the wider field of multimedia information systems, EHPS plays a crucial role in enhancing the realism and interactivity of digital content. Whether it’s creating lifelike animations, developing immersive virtual reality experiences, or enabling augmented reality applications, EHPS provides the foundation for realistic human representations. By scaling up EHPS and developing generalist foundation models, we can expect even more advanced and realistic multimedia systems in the future.