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Kwang Moo Yi

@kwangmoo_yi4,459 subscribers

Assistant Professor of Computer Science at the University of British Columbia. I also post my daily finds on arxiv.

Shorts

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.

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.

127,780 Aufrufe

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.

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.

46,846 Aufrufe

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.

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 Aufrufe

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.

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.

10,890 Aufrufe

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