>State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild (LFW) dataset. However, these models are trained on large-scale datasets that contain millions of real human face images collected from the internet. Web-crawled face images are severely biased (in terms of race, lighting, make-up, etc) and often contain labeling noise. Most importantly, these face images are collected without explicit consent, raising more pressing privacy and ethical concerns. To avoid the problems associated with real face datasets, we introduce a large-scale synthetic dataset for face recognition, obtained by photo-realistic rendering of diverse and high-quality digital faces using a computer graphics pipeline. We compare our method to SynFace, a recent method trained on GAN-generated synthetic faces, and reduce the error rate on LFW by 52.5% (accuracy from 91.93% to 96.17%). We first demonstrate that aggressive data augmentation can significantly help reduce the domain-gap between our synthetic faces and real face images. Taking advantage of having full control over the rendering pipeline, we also study how each attribute (e.g., variation in facial pose, accessories, and textures) affects the accuracy. Finally, by fine-tuning the network on a smaller number of real face images that could reasonably be obtained with consent, we achieve accuracy that is comparable to the methods trained on millions of real face images, while alleviating the problems associated with large datasets. microsoft.github.io
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>video presentation: youtube.com
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>paper: arxiv.org

>Scene Synthesis from Human Motion
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>Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly. Meanwhile, human motion alone contains rich information about the scene they reside in and interact with. For example, a sitting human suggests the existence of a chair, and their leg position further implies the chair’s pose. In this paper, we propose to synthesize diverse, semantically reasonable, and physically plausible scenes based on human motion. Our framework, Scene Synthesis from HUMan MotiON (SUMMON), includes two steps. It first uses ContactFormer, our newly introduced contact predictor, to obtain temporally consistent contact labels from human motion. Based on these predictions, SUMMON then chooses interacting objects and optimizes physical plausibility losses; it further populates the scene with objects that do not interact with humans. Experimental results demonstrate that SUMMON synthesizes feasible, plausible, and diverse scenes and has the potential to generate extensive human-scene interaction data for the community. https://lijiaman.github.io/projects/summon/