H
Hao Li
Researcher at Alibaba Group
Publications - 225
Citations - 14999
Hao Li is an academic researcher from Alibaba Group. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 56, co-authored 221 publications receiving 10232 citations. Previous affiliations of Hao Li include University of Southern California & Institute for Creative Technologies.
Papers
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Proceedings ArticleDOI
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
TL;DR: Pixel-aligned Implicit Function (PIFu) as mentioned in this paper aligns pixels of 2D images with the global context of their corresponding 3D object to produce highresolution surfaces including largely unseen regions such as the back of a person.
Proceedings ArticleDOI
High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis
TL;DR: This work proposes a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network.
Journal ArticleDOI
Learning a model of facial shape and expression from 4D scans
TL;DR: Faces Learned with an Articulated Model and Expressions is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model and is compared to these models by fitting them to static 3D scans and 4D sequences using the same optimization method.
Proceedings ArticleDOI
Realtime performance-based facial animation
TL;DR: A novel face tracking algorithm that combines geometry and texture registration with pre-recorded animation priors in a single optimization is introduced that demonstrates that compelling 3D facial dynamics can be reconstructed in realtime without the use of face markers, intrusive lighting, or complex scanning hardware.
Proceedings ArticleDOI
Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning
TL;DR: This work proposes a truly differentiable rendering framework that is able to directly render colorized mesh using differentiable functions and back-propagate efficient supervision signals to mesh vertices and their attributes from various forms of image representations, including silhouette, shading and color images.