Christian Diller

Check out our #CVPR'24 papers on 3D human interactions, generative 3D modeling, and uncertainty-aware and unsupervised 3D semantic scene understanding! Congrats to @craigleili @david_roz_ @chrdiller @yawarnihal @shivangi2201 @jiapeng_tang @AnhQuanCAO for their amazing work!

(1/2) Check out DPHMs: Diffusion Parametric Head Models for Depth-based Tracking! 3D head reconstruction from noisy & sparse depth? Our diffusion prior constrains identity/expression latents, and maps them to high-quality samples. youtu.be/w_EJ5LDJ7T4 tangjiapeng.github.io/projects/DPHMs/

(1/2) Check out 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧𝐀𝐯𝐚𝐭𝐚𝐫𝐬: Photorealistic Head Avatars with Rigged 3D Gaussians! We create photorealistic head avatars by animating 3D Gaussians on a parametric face model - edited and rendered in *real-time*! youtu.be/lVEY78RwU_I shenhanqian.github.io/gaussian-avata…

Check out @DaoyiGao's DiffCAD - introducing probabilistic CAD retrieval and alignment to an RGB image. We captures ambiguities in depth/scale, inexact CAD matches, and don't require any training on real data! daoyig.github.io/DiffCAD/ youtu.be/PCursyPosMY

(1/2) 📢📢𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧𝐀𝐯𝐚𝐭𝐚𝐫𝐬 📢📢 High-fidelity 3D head avatars with precise control over viewpoint, expression, and pose. -> Our parametric 3D model enables control & consistency + 2D diffusion makes it photoreal. youtu.be/nSjDiiTnp2E tobias-kirschstein.github.io/diffusion-avat…

Can we synthesize 3D human-scene interactions without learning from any 3D data? Yes! Check out @craigleili's GenZI, a novel zero-shot approach to generating 3D interactions by distilling priors from large vision-language models. craigleili.github.io/projects/genzi/ youtu.be/ozfs6E0JIMY

Starting today, #CVPR2024 Area Chairs will begin the process of recommending reviewers for each of the 11,523 valid submissions. We expect to distribute paper assignments to reviewers on December 9th!

(1/2) 📢📢𝗦𝗰𝗲𝗻𝗲𝗧𝗲𝘅 📢📢 Given scene geometry and text prompt -> SceneTex generates high-quality textures. Main idea: directly optimize scene texture with gradients from a score-distillation objective with view sampling. youtube.com/watch?v=-x8oMe… daveredrum.github.io/SceneTex/

We generate 3D full-body human-object interactions just from text and object geometry. Explicitly modeling contact is key for realistic interactions :)

Excited to have a distinguished guest lecture today by Turing Award laureate, three time Academy Award recipient Pat Hanrahan, speaking on programmable graphics today at @TU_Muenchen! For TUM students, check it out online here: live.rbg.tum.de/w/I2DL/36360

(1/2) Check out 𝐌𝐞𝐬𝐡𝐆𝐏𝐓! MeshGPT generates triangle meshes by autoregressively sampling from a transformer model that produces tokens from a learned geometric vocabulary. As a result, we obtain clean and compact meshes :) nihalsid.github.io/mesh-gpt/ youtu.be/UV90O1_69_o

Diffusion models are awesome! Check out our survey on 𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 𝐟𝐨𝐫 𝐕𝐢𝐬𝐮𝐚𝐥 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠! We give an introduction to diffusion models and highlight how they are used by state-of-the-art methods in graphics and vision. arxiv.org/abs/2310.07204

Check out @ABokhovkin's Mesh2Tex! From a real-world image and a shape mesh to texture, we generate high-res and realistic texturing without requiring any matching geometry or pose alignment to the image! w/ @shubtuls #ICCV2023 arxiv.org/abs/2304.05868 alexeybokhovkin.github.io/mesh2tex

Looking for a challenging dataset for novel view synthesis and 3D semantics? Check out ScanNet++ at #ICCV23 (Oral)! 460+ scenes w/ 1mm laser scans, semantics, DSLR images, iPhone RGBD video @chandan__yes @liuyuehcheng @MattNiessner arxiv.org/abs/2308.11417 cy94.github.io/scannetpp

Camera frame registration with little or no overlap? Check out @cangumeli's ObjectMatch: We leverage object semantics to find indirect correspondences between frames. Great for any SLAM and SfM pose optimization! Vid: youtu.be/kuXoKVrzURk #CVPR2023 @angelaqdai

(1/2) Excited to share "Learning Neural Parametric Head Models" #CVPR2023! We capture over 5200 high-quality 3D human head scans from which we build a neural parametric head model that disentangles & expressions and deformations. simongiebenhain.github.io/NPHM/ youtu.be/0mDk2tFOJCg

📢Panoptic Lifting for 3D Scene Understanding with Neural Fields #CVPR23 highlight! Given only posed RGB images of a scene, we optimize a panoptic radiance field representing color, depth, semantics, and instances at any point in space. Vid: youtu.be/QtsiL-6rSuM @yawarnihal

On weird LaTeX packages today: I found this cute little snowman package to make your day ☃️ ctan.org/pkg/scsnowman

❓ Can a single neural model solve BOTH visual grounding and dense captioning in RGB-D scans? ✅ We present a self-critical speaker-listener architecture to unify both tasks. 📢 Come and chat today at 15:50 in Hall B 32 #ECCV2022

Gonna present a new benchmark ScanNet200 and our language grounded pretraining strategy at today's @ECCV2022 poster session starting at 11 am! A huge thanks for my amazing supervisors @angelaqdai and @orlitany! Come and chat if you are interested