Christian Richardt @c_richardt
Research Scientist at @RealityLabs. Working on novel-view synthesis etc. Previously @UniofBath, #IVCI @Saar_Uni, #MPI_Informatik, @Inria, @Cambridge_Uni. richardt.name Zurich, Switzerland Joined January 2012-
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This is from the team I joined in June! This is only the start of the immersive scenes vision we have. 😁 If you're interested in working with us, feel free to reach out! 🥽🙂 Some of us will be at #ICCV2025 as well for a relevant workshop! scenecomp.github.io 🏝️
This is from the team I joined in June! This is only the start of the immersive scenes vision we have. 😁 If you're interested in working with us, feel free to reach out! 🥽🙂 Some of us will be at #ICCV2025 as well for a relevant workshop! scenecomp.github.io 🏝️
Introducing: Hyperscape Capture 📷 Last year we showed the world's highest quality Gaussian Splatting, and the first time GS was viewable in VR. Now, capture your own Hyperscapes, directly from your Quest headset in only 5 minutes of walking around. meta.com/experiences/87…
Introducing: Hyperscape Capture 📷 Last year we showed the world's highest quality Gaussian Splatting, and the first time GS was viewable in VR. Now, capture your own Hyperscapes, directly from your Quest headset in only 5 minutes of walking around. meta.com/experiences/87… https://t.co/BPV8g5AXFn
Interested in reviewing for @CVPR 2026? We are looking for anyone with prior publication experience at venues like CVPR that is interested and may not be on our list of reviewers yet. If that is you, please reach out!
💡 Introducing LuxDiT: a diffusion transformer (DiT) that estimates realistic scene lighting from a single image or video. It produces accurate HDR environment maps, addressing a long-standing challenge in computer vision. 🔗Paper: arxiv.org/abs/2509.03680
Introducing DINOv3: a state-of-the-art computer vision model trained with self-supervised learning (SSL) that produces powerful, high-resolution image features. For the first time, a single frozen vision backbone outperforms specialized solutions on multiple long-standing dense…
High quality meme.
You know what has actually disappeared? The support structure to train someone to master the craft. This goes for almost all things. Yes, including engineering and design jobs. Everyone wants masters but no one wants to train apprentices.
You know what has actually disappeared? The support structure to train someone to master the craft. This goes for almost all things. Yes, including engineering and design jobs. Everyone wants masters but no one wants to train apprentices.
📢📢We have a last-minute internship opening on my team at @NVIDIAAI for this summer. If you are interested and have experience with large feedforward reconstruction models or post-training image/video diffusion models, please get in touch!
The recordings from the #CVPR2025 tutorial "Volumetric Video in the Real World" are now available! nvlabs.github.io/cvpr2025-volum…
The recordings from the #CVPR2025 tutorial "Volumetric Video in the Real World" are now available! nvlabs.github.io/cvpr2025-volum…
Recording of the workshop is now online, big thanks to all the organizers and everyone who attended both in person and online! neural-bcc.github.io
Recording of the workshop is now online, big thanks to all the organizers and everyone who attended both in person and online! neural-bcc.github.io
🔥 Very cool to bring paintings to life with Kling 2.1 while visiting the @NationalGallery in London 🇬🇧 Made with @PhotoChatAI
Excited to share our work at #CVPR2025! 👁️IRIS estimates accurate surface material, spatially-varying HDR lighting, and camera response function given a set of LDR images! It enables realistic, view-consistent, and controllable relighting and object insertion. (links in 🧵)
Join our #CVPR2025 Tutorial on Volumetric Video in the Real World on 6/11! With amazing speakers: @holynski_ @c_richardt @iamborisi @yiyi_liao_ @sharathgirish97 & Lan Xu ⏲️Wed, 6/11, 9am - 5pm CDT 🏢Room 205B, Music City Center, Nashville TN @CVPR 🔗nvlabs.github.io/cvpr2025-volum…
Geometry-guided Online 3D Video Synthesis with Multi-View Temporal Consistency Hyunho Ha, @leixiao08, @c_richardt, @thunguyenphuoc, @_ChangilKim, @MinHKim_KAIST, @douglaslanman, Numair Khan tl;dr: TSDF in synthesized view’s image space->depth maps->image-based blending…
Time of the Flight of the Gaussians: Optimizing Depth Indirectly in Dynamic Radiance Fields Runfeng Li, Mikhail Okunev, Zixuan Guo, Anh Ha Duong, @c_richardt, Matthew O'Toole, @jtompkin tl;dr: occupancy bias+low-reflectivity bias->discrepancy problem between the rendered mean…
Time of the Flight of the Gaussians: Optimizing Depth Indirectly in Dynamic Radiance Fields Problem: In continuous-wave time-of-flight (C-ToF) radiance field reconstruction, the property of interest—depth—is not directly measured, causing an additional challenge. This problem…

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