Sign in

Thomas Wimmer

@wimmerthomas.bsky.social
411 followers 139 following 31 posts

PhD Candidate at the Max Planck ETH Center for Learning Systems working on 3D Computer Vision. wimmerth.github.io

PostsRepliesMedia
Reposted by Thomas Wimmer
Julien Gaubil @jgaubil.bsky.social · 04/11/2025
DUSt3R et al. are impressive, but how do they actually work? We investigate this in our project 𝘜𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘔𝘶𝘭𝘵𝘪-𝘝𝘪𝘦𝘸 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴!⁣ We share findings on the iterative nature of reconstruction, the roles of cross and self-attention, and the emergence of correspondences across the network [1/8] ⬇️
174
Reposted by Thomas Wimmer
Computer Vision and Machine Learning at MPI Informatics @cvml.mpi-inf.mpg.de · 19/10/2025
ICCV 2025 🌺 Aloha from Hawaii! MPI-INF (D2) is presenting 4 papers this year (one Highlight). Thread 👇
1126
Thomas Wimmer @wimmerthomas.bsky.social · 18/10/2025
I am on my way to #ICCV2025 to present DIY-SC, where we refine foundational features for better semantic correspondence performance. Please come by our poster poster #538 (Session 2) if you're interested or want to chat about my latest project, AnyUp!
060
Reposted by Thomas Wimmer
Johannes Schusterbauer @joh-schb.bsky.social · 17/10/2025
🤔 What if you could generate an entire image using just one continuous token? 💡 It works if we leverage a self-supervised representation! Meet RepTok🦎: A generative model that encodes an image into a single continuous latent while keeping realism and semantics. 🧵 👇
1104
Thomas Wimmer @wimmerthomas.bsky.social · 16/10/2025
Try it out now! Code and model weights are public. 💻 Code: github.com/wimmerth/anyup Great collaboration with Prune Truong, Marie-Julie Rakotosaona, Michael Oechsle, Federico Tombari, Bernt Schiele, and @janericlenssen.bsky.social! CC: @cvml.mpi-inf.mpg.de @mpi-inf.mpg.de
wimmerth.github.io
AnyUp
Universal Feature Upsampling
030
Thomas Wimmer @wimmerthomas.bsky.social · 16/10/2025
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.
131
Thomas Wimmer @wimmerthomas.bsky.social · 16/10/2025
When performing linear probing for semantic segmentation or normal and depth estimation, AnyUp consistently outperforms prior upsamplers. Importantly, the upsampled features also stay faithful to the input feature space, as we show in experiments with pre-trained DINOv2 probes.
110
Thomas Wimmer @wimmerthomas.bsky.social · 16/10/2025
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.
110
Thomas Wimmer @wimmerthomas.bsky.social · 16/10/2025
Foundation models like DINO or CLIP are used in almost all modern computer vision applications. However, their features are of low resolution and many applications need pixel-wise features instead. AnyUp can upsample any features of any dimensionality to any resolution.
110
Thomas Wimmer @wimmerthomas.bsky.social · 16/10/2025
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/
2285
Reposted by Thomas Wimmer
Phillip Isola @phillipisola.bsky.social · 10/10/2025
Suppose you have separate datasets X, Y, Z, without known correspondences. We do the simplest thing: just train a model (e.g., a next-token predictor) on all elements of the concatenated dataset [X,Y,Z]. You end up with a better model of dataset X than if you had trained on X alone! 6/9
Architecture for Unpaired Multimodal Learner.
2231
Thomas Wimmer @wimmerthomas.bsky.social · 07/10/2025
Happy to find my name on the list of outstanding reviewers :] Come and check out our poster on learning better features for semantic correspondence in Hawaii! 📍 Poster #538 (Session 2) 🗓️ Oct 21 | 3:15 – 5:00 p.m. HST genintel.github.io/DIY-SC
030
Thomas Wimmer @wimmerthomas.bsky.social · 21/08/2025
What was the patch size used here?
100
Thomas Wimmer @wimmerthomas.bsky.social · 26/06/2025
All the links can be found here. Great collaborators! bsky.app/profile/odue...
020
Thomas Wimmer @wimmerthomas.bsky.social · 26/06/2025
🚀 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!
1102
Thomas Wimmer @wimmerthomas.bsky.social · 11/06/2025
The CVML group at the @mpi-inf.mpg.de has been busy for CVPR. Check out our papers and come by the presentations!
041
Reposted by Thomas Wimmer
Computer Vision and Machine Learning at MPI Informatics @cvml.mpi-inf.mpg.de · 09/04/2025
Hello world, we are now on Bluesky 🦋! Follow us to receive updates on exciting research and projects from our group! #computervision #machinelearning #research
0114
Thomas Wimmer @wimmerthomas.bsky.social · 28/03/2025
We only use open-sourced models and the implementation of our method is readily available. Please check out the paper website for more details: wimmerth.github.io/gaussians2li...
wimmerth.github.io
Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes
We introduce a method to animate given 3D scenes that uses pre-trained models to lift 2D motion into 3D. We propose a training-free, autoregressive method to generate more 3D-consi...
010
Thomas Wimmer @wimmerthomas.bsky.social · 28/03/2025
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. 🧵⬇️
100
Thomas Wimmer @wimmerthomas.bsky.social · 28/03/2025
While we can now transfer motion into 3D, we still have to deal with a fundamental problem: Lacking 3D consistency of generated videos. With limited resources, we can't fine-tune or retrain a VDM to be pose-conditioned. Thus, we propose a zero-shot technique to generate more 3D-consistent videos! 🧵⬇️
Improvement of multi-view consistency of generated videos through latent interpolation. In addition to the rendering of the dynamic scene f, using the rendering function g from the current viewpoint g(f)_s, we compute the latent embedding of the warped video output v_{s-1} of the previous optimization step (from a different viewpoint). We linearly interpolate the latents before passing them through the video diffusion model (VDM), which is additionally conditioned on the static scene view from the current viewpoint. The resulting output is finally decoded to a new video output v_s.
100
Thomas Wimmer @wimmerthomas.bsky.social · 28/03/2025
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. 🧵⬇️
Method overview for lifting 2D dynamics into 3D. Pre-trained models are shown in blue. We detect 2D point tracks and use aligned estimated depth values to lift them into 3D.
The 4D (dynamic 3D) Gaussians are initialized with the static 3D scene input.
110
Thomas Wimmer @wimmerthomas.bsky.social · 28/03/2025
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. 🧵⬇️
2131
Thomas Wimmer @wimmerthomas.bsky.social · 03/03/2025
Can you do reasoning with diffusion models? The answer is yes! Take a look at Spatial Reasoning Models. Hats off for this amazing work!
030
Thomas Wimmer @wimmerthomas.bsky.social · 14/02/2025
I wonder to which degree one could artificially make real images (with GT depth) more abstract during training in order to make depth models learn these priors that we would have (like green=field, blue=sky) and whether that would actually give us any benefit, like increased robustness...
110
Thomas Wimmer @wimmerthomas.bsky.social · 14/02/2025
Ah, thanks, I overlooked that :)
110
Thomas Wimmer @wimmerthomas.bsky.social · 14/02/2025
Nice experiments! What model did you use?
110
Reposted by Thomas Wimmer
Visual Inference Lab @visinf.bsky.social · 31/01/2025
🏔️⛷️ Looking back on a fantastic week full of talks, research discussions, and skiing in the Austrian mountains!
03211
Thomas Wimmer @wimmerthomas.bsky.social · 16/01/2025
Give a warm welcome to @janericlenssen.bsky.social!
020
Thomas Wimmer @wimmerthomas.bsky.social · 15/01/2025
Well well, it turns out that GIFs aren't yet supported on this platform. Here is the teaser video as an MP4 instead:
010
Thomas Wimmer @wimmerthomas.bsky.social · 15/01/2025
This work was led by @mohammadasim98.bsky.social and is a collaboration with Christopher Wewer, Bernt Schiele and Jan Eric Lenssen. Check out the website with lots of nice visuals that show how our metric works and use it in your next diffusion model project! geometric-rl.mpi-inf.mpg.de/met3r/
geometric-rl.mpi-inf.mpg.de
MEt3R
Measuring Multi-View Consistency in Generated Images.
021
Thomas Wimmer @wimmerthomas.bsky.social · 15/01/2025
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.
100
Thomas Wimmer @wimmerthomas.bsky.social · 15/01/2025
Especially for video generation methods where no ground truth camera poses are given, our proposed metric can help to shed light on the quality of the generated videos, rather than just reporting results from yet another human survey.
100
Thomas Wimmer @wimmerthomas.bsky.social · 15/01/2025
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...
100
Thomas Wimmer @wimmerthomas.bsky.social · 15/01/2025
The MET3R scores correlate well with the 3D awareness of different multi-view image generation methods, as we show in our experiments. The metric is also differentiable, which means you could use it even for training! The code is easy to run and already open-sourced! github.com/mohammadasim...
100
Thomas Wimmer @wimmerthomas.bsky.social · 15/01/2025
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.
152
Thomas Wimmer @wimmerthomas.bsky.social · 15/01/2025
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 🧵
220
Reposted by Thomas Wimmer
Dmytro Mishkin @ducha-aiki.bsky.social · 15/01/2025
MEt3R: Measuring Multi-View Consistency in Generated Images Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, Jan Eric Lenssen tl;dr: DUSt3R + DINO + FeatUp together want to be FID for multiview generation arxiv.org/abs/2501.06336
1161
Thomas Wimmer @wimmerthomas.bsky.social · 14/01/2025
010
Reposted by Thomas Wimmer
Chris Offner @chrisoffner3d.bsky.social · 21/11/2024
After my general computer vision starter pack is now full (150/150 entries reached), here is one specific to 3D Vision: go.bsky.app/Cfm9XFe
1010529