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Shubham Tulsiani

@shubhtuls • 3,429 subscribers

Assistant Professor in the Robotics Institute, Carnegie Mellon University I want to build perception systems that can understand the physical world

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[1/6] What representation comes to mind when you think of a ‘camera’? Perhaps an extrinsic + intrinsic matrix? In our ICLR (oral) paper, we instead infer a distributed representation where each pixel is associated with a ray, and show SoTA results for few-view pose estimation.

[1/6] What representation comes to mind when you think of a ‘camera’? Perhaps an extrinsic + intrinsic matrix? In our ICLR (oral) paper, we instead infer a distributed representation where each pixel is associated with a ray, and show SoTA results for few-view pose estimation.

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[1/6] Our #CVPR2025 paper “DiffusionSfM” extends our RayDiffusion framework — inferring both geometry and cameras via diffusing pixelwise ray origins and endpoints.

[1/6] Our #CVPR2025 paper “DiffusionSfM” extends our RayDiffusion framework — inferring both geometry and cameras via diffusing pixelwise ray origins and endpoints.

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[1/N] Rotary Position Embeddings (RoPE) are ubiquitous across transformers that process tokens from 1D, 2D, or 3D grids e.g. language, images, or videos. Our RayRoPE formulation extends these to multi-view transformers. Paper and code:
0:41
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[1/N] Rotary Position Embeddings (RoPE) are ubiquitous across transformers that process tokens from 1D, 2D, or 3D grids e.g. language, images, or videos. Our RayRoPE formulation extends these to multi-view transformers. Paper and code:

Shubham Tulsiani

55,859 просмотров • 4 месяцев назад

[1/N] We present a plug-and-play mechanism to controllably steer inference of any diffusion/flow model towards a sharper or flatter sampling distribution, resulting in improvements across domains e.g. text-to-image (10% FID reduction), protein generation (improved designability).
0:51
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[1/N] We present a plug-and-play mechanism to controllably steer inference of any diffusion/flow model towards a sharper or flatter sampling distribution, resulting in improvements across domains e.g. text-to-image (10% FID reduction), protein generation (improved designability).

Shubham Tulsiani

60,777 просмотров • 8 месяцев назад

[1/N] Current visual geometry prediction models primarily rely on labeled 3D data. Our CVPR26 paper, Flow3r, allows additionally leveraging unlabeled videos (using flow supervision) for scalable visual geometry learning, enabling accurate multi-view 3D reconstruction in-the-wild.
0:31
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[1/N] Current visual geometry prediction models primarily rely on labeled 3D data. Our CVPR26 paper, Flow3r, allows additionally leveraging unlabeled videos (using flow supervision) for scalable visual geometry learning, enabling accurate multi-view 3D reconstruction in-the-wild.

Shubham Tulsiani

15,974 просмотров • 3 месяцев назад

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