There's a problem with current 3D gesture generation from speech: they don't account for emotion, leading to less expressive animations. AMUSE presented at #CVPR2024, maps speech to 3 latent vectors—content, emotion, & personal style—allowing for more expressive animations. (1/8)

"Gaussian Eigen Models for Human Heads" by Wojciech Zielonka et al. Project: zielon.github.io/gem/ Video(better quality): youtube.com/watch?v=VuLI0F… Method ⬇️ 1 | 2

I am deeply honored to share that my project ‘Learning Digital Humans in Motion’ (LeMo) has been awarded an #ERC Starting Grant! There will be a couple of open positions (PhD students, postdocs) soon to join the project at TU Darmstadt . tu-darmstadt.de/universitaet/a…

Congrats Soubhik!

Many people are in the middle of the #CVPR2025 deadline. So I'm sharing my guide to writing a CVPR paper (or any paper). My students have had this for years but I haven't shared it publicly before. I hope you find it useful and write a great paper. #CVPR2025 medium.com/@black_51980/w…

📢 I am #hiring 2x #PhD candidates to work on Human-centric #3D #ComputerVision at the University of #Amsterdam! 📢 The positions are funded by an #ERC #StartingGrant. For details and for submitting your application please see: werkenbij.uva.nl/en/vacancies/p… 🆘 Deadline: Feb 16 🆘

Check out the changes for #ICCV2025 🌶️ iccv.thecvf.com/Conferences/20…

📢📢𝐍𝐞𝐑𝐒𝐞𝐦𝐛𝐥𝐞 𝐯𝟐 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐑𝐞𝐥𝐞𝐚𝐬𝐞📢📢 Head captures of 7.1MP from 16 cameras at 73fps: * More recordings (425 people) * Better color calibration * Convenient download scripts github.com/tobias-kirschs… The new version of our dataset adds 156

📢Announcing our 3D head avatar benchmark📢 Two tasks with hidden test sets: - Dynamic Novel View Synthesis on Heads - Monocular FLAME-driven Head Avatar Reconstruction Our goal is to make research on 3D head avatars more comparable and ultimately increase the realism of

Can we reconstruct relightable human hair appearance from real-world visual observations? We introduce GroomLight, a hybrid inverse rendering method for relightable human hair appearance modeling. syntec-research.github.io/GroomLight/

🎉 I’m happy to share that 𝗯𝗼𝘁𝗵 𝗚𝗘𝗠 𝗮𝗻𝗱 𝗦𝘆𝗻𝗦𝗵𝗼𝘁 got accepted to 𝗖𝗩𝗣𝗥 𝟮𝟬𝟮𝟱! Check out the papers, code, and comparisons below: 🔹 GEM: github.com/Zielon/GEM 🔹 SynShot: github.com/Zielon/SynShot Thanks to all collaborators, and see you in Nashville! 👋

📢 SHeaP: Self-Supervised Head Predictor Learned via 2D Gaussians 📢 Given a single input image, we predict accurate 3D head geometry, pose, and expression. Previous works (e.g. DECA, EMOCA) use differentiable mesh rasterization to learn a self-supervised head geometry

Here's my 3DV talk, in chapters: 1) Intro / NeRF boilerplate. 2) Recent reconstruction work. 3) Recent generative work. 4) Radiance fields as a field. 5) Why generative video has bitter-lessoned 3D. 6) Why generative video hasn't bitter-lessoned 3D. 5 & 6 are my favorites.

📢Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction📢 -> highly accurate face reconstruction by training powerful VITs via surface normals and UV-coordinates estimation. The geometric cues from our 2D foundation model backbone constrain the 3DMM

Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction TLDR: DINO feature extraction + ViTs predict uv and normal maps, which allow for very accurate 2d landmark tracking and so powerful image/video monocular reconstruction. 📽️ simongiebenhain.github.io/pixel3dmm/

We had an incredible week at Schloss Dagstuhl for the seminar on Generative Models for 3D Vision! Great talks and lots of inspiration on the future of 3D vision and generative models 🚀

1/n Proud to present our latest attempt to give 3D faces a voice: “THUNDER: Supervising 3D Talking Head Avatars with Analysis-by-Audio-Synthesis” We synthesize audio from facial motion! Website: thunder.is.tue.mpg.de With Michael Black, Senya Polikovsky, Carolin Schmitt

Excited to present two projects tomorrow together with Timo Bolkart (Timo Bolkart) at #CVPR2025 in Nashville🎸🇺🇸! 📍 SYNSHOT - Poster 8, Sat, June 14, 10:30–12:30 (Session 3, Hall D) 📍 GEM - Poster 7, Sat, June 14, 17:00–19:00 (Session 4, Hall D) Come say hi!

SYNSHOT - Synthetic Prior for FewShot Drivable Head Avatar Inversion is a method that explores how a synthetic dataset can be used to build a prior for few-shot inversion of 4D avatars using in-the-wild images. 💻 Code: github.com/Zielon/synshot 🌐 Website: zielon.github.io/synshot

GEM - Gaussian Eigen Models for Human Heads is a method that distills networks into lightweight linear avatars represented as an efficient eigenbasis, offering easy adjustability in terms of memory and size. 💻 Code: github.com/Zielon/GEM 🌐 Website: zielon.github.io/gem