About Me

I was a research scientist at Apple, leading an applied research team with a focus on neural rendering and generative AI. I did my Ph.D. at MIT and my undergrad at HKUST.

Shipped Products

A number of products that I contributed to:

• Depth estimation on ARKit
• Depth estimation on Vision Pro
• Face reconstruction for Persona (3D Facetime on Vision Pro)
• 24MP imaging on iPhone Pro

Research Highlights

I’m broadly interested in computer vision, machine learning, and computer graphics. Much of my research is about neural rendering, 3D reconstruction, and generative models. A complete list of my publication can be found on my Google Scholar.

	HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion Ziya Erkoç, Fangchang Ma, Qi Shan, Matthias Nießner, Angela Dai ICCV, 2023 [arXiv] [webpage]
	Generative Multiplane Images: Making a 2D GAN 3D-Aware Xiaoming Zhao, Fangchang Ma, David Güera, Zhile Ren, Alexander Schwing, Alex Colburn ECCV, 2022 (Oral Presentation) [arXiv] [code] [webpage]
	Texturify: Generating Textures on 3D Shape Surfaces Yawar Siddiqui, Justus Thies, Fangchang Ma, Qi Shan, Matthias Nießner, Angela Dai ECCV, 2022 [arXiv] [video] [webpage]
	Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera Fangchang Ma, Guilherme Venturelli Cavalheiro, Sertac Karaman ICRA, 2019 [arXiv] [code] [video]
	FastDepth: Fast Monocular Depth Estimation on Embedded Systems Diana Wofk*, Fangchang Ma*, Tien-Ju Yang, Sertac Karaman, Vivienne Sze ICRA, 2019 [arXiv] [code] [video] [webpage]
	Invertibility of Convolutional Generative Networks from Partial Measurements Fangchang Ma, Ulas Ayaz, Sertac Karaman NeurIPS, 2018 [pdf] [supplementary] [code]
	Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image Fangchang Ma, Sertac Karaman ICRA, 2018 [arXiv] [video] [pytorch code] [torch code]