Thomas Wimmer

Super excited to introduce ✨ AnyUp: Universal Feature Upsampling 🔎 Upsample any feature - really any feature - with the same upsampler, no need for cumbersome retraining. SOTA feature upsampling results while being feature-agnostic at inference time. 🌐 wimmerth.github.io/anyup/

Generalization: AnyUp is the first learned upsampler that can be applied out-of-the-box to other features of potentially different dimensionality. In our experiments, we show that it matches encoder-specific upsamplers and that trends between different model sizes are preserved.

Super excited to introduce ✨ AnyUp: Universal Feature Upsampling 🔎 Upsample any feature - really any feature - with the same upsampler, no need for cumbersome retraining. SOTA feature upsampling results while being feature-agnostic at inference time. 🌐 wimmerth.github.io/anyup/

AnyUp is a lightweight model that uses a feature-agnostic layer to obtain a canonical representation that is independent of the input dimensionality. Together with window attention-based upsampling, a new training pipeline and consistency regularization, we achieve SOTA results.

Super excited to introduce ✨ AnyUp: Universal Feature Upsampling 🔎 Upsample any feature - really any feature - with the same upsampler, no need for cumbersome retraining. SOTA feature upsampling results while being feature-agnostic at inference time. 🌐 wimmerth.github.io/anyup/

🚀 Just accepted to ICCV 2025! In DIY-SC, we improve foundational features using a light-weight adapter trained with carefully filtered and refined pseudo-labels. 🔧 Drop-in alternative to plain DINOv2 features! 📦 Code + pre-trained weights available now. 🔥 Try it in your next vision project!

Had the honor to present "Gaussians-to-Life" at #3DV2025 yesterday. In this work, we used video diffusion models to animate arbitrary 3D Gaussian Splatting scenes. This work was a great collaboration with @moechsle.bsky.social, @miniemeyer.bsky.social, and Federico Tombari. 🧵⬇️

We can animate arbitrary 3D scenes within 10 minutes on a RTX4090 while keeping scene appearance and geometry in tact. Note, that since the time I worked on this, open-sourced video diffusion models have improved significantly, which will directly improve the results of this method as well. 🧵⬇️

Had the honor to present "Gaussians-to-Life" at #3DV2025 yesterday. In this work, we used video diffusion models to animate arbitrary 3D Gaussian Splatting scenes. This work was a great collaboration with @moechsle.bsky.social, @miniemeyer.bsky.social, and Federico Tombari. 🧵⬇️

Standard practices like SDS fail for this task as VDMs provide a guidance signal that is too noisy, resulting in "exploding" scenes. Instead, we propose to employ several pre-trained 2D models to directly lift motion from tracked points in the generated videos to 3D Gaussians. 🧵⬇️

Quantitative evaluation of diffusion model outputs is hard! We realized that we are often lacking metrics for comparing the quality of video and multi-view diffusion models. Especially the quantification of multi-view 3D consistency across frames is difficult. But not anymore: Introducing MET3R 🧵

Quantitative evaluation of diffusion model outputs is hard! We realized that we are often lacking metrics for comparing the quality of video and multi-view diffusion models. Especially the quantification of multi-view 3D consistency across frames is difficult. But not anymore: Introducing MET3R 🧵

Important note: Our metric is not here to measure the visual quality / appearance of generated content. It is, instead, meant to act orthogonal to existing image quality metrics by focusing on the 3D consistency of generated frames.

Quantitative evaluation of diffusion model outputs is hard! We realized that we are often lacking metrics for comparing the quality of video and multi-view diffusion models. Especially the quantification of multi-view 3D consistency across frames is difficult. But not anymore: Introducing MET3R 🧵

Speaking of multi-view diffusion models, we also trained a new open-source multi-view latent diffusion model built on top of Stable Diffusion and heavily inspired by the closed-source CAT3D model. Weights and code are already public. Check it out! github.com/mohammadasim...

Quantitative evaluation of diffusion model outputs is hard! We realized that we are often lacking metrics for comparing the quality of video and multi-view diffusion models. Especially the quantification of multi-view 3D consistency across frames is difficult. But not anymore: Introducing MET3R 🧵

We propose MET3R, a new metric for measuring multi-view consistency in generated images. Our method is built upon DUSt3R and evaluates the consistency of projected DINO features between two views. It is able to accurately capture the 3D consistency in generated images.