Author name cluster

He Wen

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2 papers

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NeurIPS Conference 2024 Conference Paper

Codec Avatar Studio: Paired Human Captures for Complete, Driveable, and Generalizable Avatars

Julieta Martinez
Emily Kim
Javier Romero
Timur Bagautdinov
Shunsuke Saito
Shoou-I Yu
Stuart Anderson
Michael Zollhöfer

To build photorealistic avatars that users can embody, human modelling must be complete (cover the full body), driveable (able to reproduce the current motion and appearance from the user), and generalizable ( i. e. , easily adaptable to novel identities). Towards these goals, paired captures, that is, captures of the same subject obtained from systems of diverse quality and availability, are crucial. However, paired captures are rarely available to researchers outside of dedicated industrial labs: Codec Avatar Studio is our proposal to close this gap. Towards generalization and driveability, we introduce a dataset of 256 subjects captured in two modalities: high resolution multi-view scans of their heads, and video from the internal cameras of a headset. Towards completeness, we introduce a dataset of 4 subjects captured in eight modalities: high quality relightable multi-view captures of heads and hands, full body multi-view captures with minimal and regular clothes, and corresponding head, hands and body phone captures. Together with our data, we also provide code and pre-trained models for different state-of-the-art human generation models. Our datasets and code are available at https: //github. com/facebookresearch/ava-256 and https: //github. com/facebookresearch/goliath.

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IROS Conference 2009 Conference Paper

Path planning in changing environments by using optimal path segment search

Hong Liu 0008
He Wen
Yan Li

This paper presents a novel planner for manipulators and robots in changing environments. When environments are complicated, it's always difficult to find a completely valid path solution, which is essential for many methods. However, our planner searches for several path segments to make robot move towards its goal as much as possible even though such a complete solution doesn't exist currently. In the learning phase, the planner begins by building a roadmap that captures the topological structure of the configuration space in a workspace without obstacles. In the query phase, the planner searches for a solution path in the roadmap with the A* algorithm and performs roadmap updating using the lazy evaluation idea concurrently with the solution search process. If a completely valid solution is found, it will be adopted immediately. Otherwise the planner will collect a set of maximum valid path segments and then select the optimal one for planning in the execution process. The searching and execution process will be repeatedly performed until a goal configuration is reached. In plentiful experiments, our planner shows promising performances.

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