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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.
https://t.co/GHrC7NTUvb
https://t.co/1zK8ntsNHq
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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*!
https://t.co/R90VnWJB9Y
https://t.co/Gv5gED01SG
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!
https://t.co/eFZgP9JP4j
https://t.co/giMJl6R9Kl
(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.
https://t.co/XCmGtehL07
https://t.co/nTBuBFaDlY
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.
https://t.co/2zAgsqgXvD
https://t.co/aMWmc2tjVs
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!
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📢📢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.
https://t.co/Rf1YLit6Qo
https://t.co/AKNpQDKOUX
We generate 3D full-body human-object interactions just from text and object geometry.
Explicitly modeling contact is key for realistic interactions :)
Check out @chrdiller's CG-HOI :)
We generate realistic 3D human-object interactions, from object geometry and text description.
A key ingredient is explicit modeling of contact, during training and as guidance during inference.
https://t.co/Cl5Jw9oFBO
https://t.co/FVIFqEpjHi
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: https://t.co/S4IqztI8aB
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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 :)
https://t.co/ynrf0qjYVF
https://t.co/rQe7ipP15t
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.
https://t.co/FqaqF7tMPM
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
https://t.co/mdaErke58q
https://t.co/U8UV5YKTKJ
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
https://t.co/3oe5yXUEkM
https://t.co/8WCLF7C9n4
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: https://t.co/NowioCRTqq
#CVPR2023@angelaqdai
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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.
https://t.co/ynlcqd8ynr
https://t.co/npsZulVUHO
📢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: https://t.co/vFpb02zsZQ
@yawarnihal
❓ 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