Chris Rockwell

I am a Ph.D. candidate in Computer Science and Engineering at the University of Michigan. I do research in computer vision and machine learning and am advised by David F. Fouhey and Justin Johnson. I have been fortunate to collaborate with others, including: JJ Park and Honglak Lee at UM, Jia-Bin Huang at Meta Reality Labs and Jia Deng at Princeton. I'm currently a research intern hosted by Chen-Hsuan Lin at NVIDIA's Deep Imagination Research group.

Please feel free to contact me: cnris at umich dot edu

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Our flexible method produces accurate and robust pose estimates using complementary strengths of Correspondence + Solver and Learning-Based methods.

Pretrained models are highly effective at 3D captioning; we use them to collect large-scale 3D-text data and finetune text-to-3D models.

Small modifications to a ViT enable computations similar to the Eight-Point algorithm, and are a good inductive bias for pose estimation.

Transformers are really good at integrating evidence across multiple views and producing a planar reconstruction.

Our forward-warping-based method enables real-time, high-quality novel view synthesis on novel objects from sparse views.

By training on both video data and 3D reconstructions in the right way, we can build models of articulations of 3D objects on ordinary video data.

Combining 3D reasoning and autoregressive modeling facilitates high-quality and consistent single-image novel view synthesis.

A simple self-training framework significantly improves 3D human pose estimation on highly-truncated Internet videos.

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