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Michael Wu

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3 papers
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3

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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NeurIPS Conference 2021 Conference Paper

A Kernel-based Test of Independence for Cluster-correlated Data

  • Hongjiao Liu
  • Anna Plantinga
  • Yunhua Xiang
  • Michael Wu

The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful kernel-based statistic for assessing the generalized dependence between two multivariate variables. However, independence testing based on the HSIC is not directly possible for cluster-correlated data. Such a correlation pattern among the observations arises in many practical situations, e. g. , family-based and longitudinal data, and requires proper accommodation. Therefore, we propose a novel HSIC-based independence test to evaluate the dependence between two multivariate variables based on cluster-correlated data. Using the previously proposed empirical HSIC as our test statistic, we derive its asymptotic distribution under the null hypothesis of independence between the two variables but in the presence of sample correlation. Based on both simulation studies and real data analysis, we show that, with clustered data, our approach effectively controls type I error and has a higher statistical power than competing methods.

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