Kwang Moo Yi

@kwangmoo_yi • 4,694 subscribers

Assistant Professor of Computer Science at the University of British Columbia. I also post my daily finds on arxiv. I also created https://t.co/mr7bj9zpDH

Shorts

Elflein et al., "VGG-TTT: Offline Feed-Forward 3D Reconstruction at Scale" VGGT, but with Test-time training to compress KV space. I finally understood TTT today thanks to a collaborator. Very much reminds me of scene coordinate regression networks.

16,749 views

Baek et al., "SONIC: Spectral Optimization of Noise for Inpainting with Consistency" Initial seed noise matters. And you can optimize it without any backprop through your denoiser via good-ol linearization. Importantly, you need to do this in Fourier space.

134,362 views

Bai et al., "Positional Encoding Field" Make your RoPE encoding 3D by including a z axis, then manipulate your image by simply manipulating your positional encoding in 3D --> novel view synthesis. Neat idea.

46,880 views

Wang et al., "VGGT-Ω" Scaling up VGGT with more data, unlabelled videos, self-supervision, VLM-based data curation, and architectural simplification/enhancements.

13,083 views

Yu et al., "MosaicMem: Hybrid Spatial Memory for Controllable Video World Models" A patch-based spatial memory that you raster into views + glues to make things work.

11,170 views

Chen et al., "Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers" Train a confidence predictor for tokens and merge low-confidence ones for acceleration -> faster reconstruction with VGGT/MapAnything.

10,930 views

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Zhou et al., "PAGE-4D: Disentangled Pose and Geometry Estimation for 4D Perception" VGGT extended to dynamic scenes with a dynamic mask predictor.

Kwang Moo Yi

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Kwang Moo Yi

Shorts

Elflein et al., "VGG-TTT: Offline Feed-Forward 3D Reconstruction at Scale" VGGT, but with Test-time training to compress KV space. I finally understood TTT today thanks to a collaborator. Very much reminds me of scene coordinate regression networks.

Baek et al., "SONIC: Spectral Optimization of Noise for Inpainting with Consistency" Initial seed noise matters. And you can optimize it **without** any backprop through your denoiser via good-ol linearization. Importantly, you need to do this in Fourier space.

Bai et al., "Positional Encoding Field" Make your RoPE encoding 3D by including a z axis, then manipulate your image by simply manipulating your positional encoding in 3D --&gt; novel view synthesis. Neat idea.

Wang et al., "VGGT-Ω" Scaling up VGGT with more data, unlabelled videos, self-supervision, VLM-based data curation, and architectural simplification/enhancements.

Yu et al., "MosaicMem: Hybrid Spatial Memory for Controllable Video World Models" A patch-based spatial memory that you raster into views + glues to make things work.

Chen et al., "Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers" Train a confidence predictor for tokens and merge low-confidence ones for acceleration -&gt; faster reconstruction with VGGT/MapAnything.

Videos

Watch Anya Live

Zhou et al., "PAGE-4D: Disentangled Pose and Geometry Estimation for 4D Perception" VGGT extended to dynamic scenes with a dynamic mask predictor.

Baek et al., "SONIC: Spectral Optimization of Noise for Inpainting with Consistency" Initial seed noise matters. And you can optimize it without any backprop through your denoiser via good-ol linearization. Importantly, you need to do this in Fourier space.

Bai et al., "Positional Encoding Field" Make your RoPE encoding 3D by including a z axis, then manipulate your image by simply manipulating your positional encoding in 3D --> novel view synthesis. Neat idea.

Chen et al., "Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers" Train a confidence predictor for tokens and merge low-confidence ones for acceleration -> faster reconstruction with VGGT/MapAnything.