📢📢📢 NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud... Serialization #3DV2025 TL;DR: neural reconstruction with a simpler architecture (no linear systems to solve), and up to 3x speedup vs. voxel-based methods!show more

📢DNF: Generating 4D animations with dictionary-based neural fields! Xinyi... Zhang presents a new dictionary-based neural field for unconditional 4D generation of deforming shapes -- generating motions with high-quality shape and temporal consistency.show more

(1/2) 📢𝐍𝐏𝐆𝐀: 𝐍𝐞𝐮𝐫𝐚𝐥 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐫𝐢𝐜 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐀𝐯𝐚𝐭𝐚𝐫𝐬 📢 #SIGGRAPHAsia We... leverage a neural parametric representation to facilitate precise control over 3D Gaussians to obtain high-fidelity avatars.show more

📢Pix2NPHM: Learning to Regress NPHM Reconstructions From a Single... Image📢 We directly regress neural parametric head models (NPHMs) from a single image — fast, stable, and significantly more expressive than classical 3DMMs such as FLAME. Face tracking & 3D reconstruction are often limited by the representational capacity of PCA-based face models. By lifting NPHMs to a first-class reconstruction primitive, we enable more accurate geometry, richer expressions, and finer animation control. Pix2NPHM obtains fast and reliable NPHM reconstructions on real-world data. Inference-time optimization against surface normals and canonical point maps can further increase fidelity. Key to successful and generalized training of our ViT-based network are: (1) large-scale registration of existing 3D head datasets, and (2) self-supervised training on vast in-the-wild 2D video datasets using pseudo ground-truth surface normals. Finally, we show that geometry-aware pretraining on pixel-aligned reconstruction tasks significantly outperforms generic visual pretraining (e.g., DINO-style features) in terms of generalization. 🌍 🎥 Great work by Simon Giebenhain, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Zhe Chenshow more

Introducing Neural Developmental Programs (NDPs)🧬🧠Instead of neural networks with... fixed architectures, we allow neural networks to grow through a dynamic self-organizing process, inspired by how biological nervous systems develop👇 PDF:show more

SplatVoxel: History-Aware Novel View Streaming without Temporal Training Contributions:... • We propose a hybrid Splat-Voxel feed-forward reconstruction framework that leverages historical information to enable novel view streaming, without relying on multi-view video datasets for training. • We develop an efficient sparse voxel transformer with a coarse-to-fine voxel representation, outperforming existing feed-forward Gaussian splatting methods. • Experiment results demonstrate that our proposed framework enhances novel view synthesis for streaming scene reconstruction, providing better visual quality and reduced temporal artifacts through history-aware modeling.show more

NO FUCKING WAY SEUNGRI!!!!! #SEUNGRI NEW 📢 📢 📢... 📢 📢show more

NN-SVG: Create neural network architecture drawings parametrically! Export them... to SVG files and use them in your work!show more

📢📢Open dOTC is LIVE! 📢📢 📈 Head to the... platform to start trading and remember trading fees automatically buy and burn $SMT! 👉 #tokenization #RWAs #permissionlessRWAsshow more

NN-SVG: Create neural network architecture drawings parametrically! Export them... to SVG files and use them in your work!show more

Nuvo: Neural UV Mapping! It's super difficult to UV... map/texture atlas geometry produced by 3D reconstruction and generation pipelines. Nuvo works on all kinds of "unruly" 3D representations (NeRF, DreamFusion, etc.) and enables easy appearance editing! 1/3show more

Human Hair Reconstruction with Strand-Aligned 3D Gaussians Contributions (cited):... – We propose a new 3D line lifting scheme that uses a modified 3DGS reconstruction technique to lift 2D orientation maps into a 3D field while also providing refinement of the camera parameters; – We introduce a dual representation of hair strand polylines and 3D Gaussians to achieve differentiable rasterization of hair strands and leverage photometric constraints for strand-based hair reconstruction; – Based on these components, we propose a coarse-to-fine optimization method for prior-guided hair reconstruction that leverages both latent and explicit representations of the hairstyle.show more

#AA23 with Prasanth Neel 📢 #AA23 with Boyapati sreenu... 📢 #AA23 with Mehar Ramesh 📢 #AA23 with Linguswami 📢 #AA23 with Vetrimaran 📢 #AA23 with Lokesh kanakaraju 📢 #AA23 with SanjayLeelaBansali 📢 #AA23 with joseph 📢 Okka movie ni intha manditho chesthunnavante Allu Arjun 🙏🏻show more

DLSS 4.5 is coming to Ray Reconstruction! Massive image... quality improvements for games with Ray Reconstruction 👍show more

Chciałabym, to jakoś skomentować 📢‼️📢‼️ ale kolejny raz mnie... zatkało📢😡📢😡 Zostawiam Wam🤷📢🤷show more

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📢 Impossible Cloud Network's Node Sale is coming in... December! 📢 The countdown begins! This is your chance to become a node operator with ICN, helping to build a secure, decentralized cloud infrastructure while earning rewards for your contribution. More info on the Node Sale will be released soon, so stay tuned! 🚀show more

🚀 Excited to share our #ICLR2025 work on planning... with neural dynamics models! While our lab has developed diverse neural dynamics models for manipulating rigid, deformable, and granular objects, having the model alone doesn’t solve the problem—planning with it remains a challenge. 💡 Enter BaB-ND, led by Keyi and Jiangwei! We propose a scalable, GPU-accelerated branch-and-bound algorithm, inspired by neural network verification, to enable effective planning for diverse objects modeled with neural dynamics. 🔗 Project page (open-source + detailed docs!): 🎥 Watch the video to see T being pushed around obstacles, and check out Keyi’s thread for more details!show more

Lightplane Highly-Scalable Components for Neural 3D Fields Contemporary 3D... research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing ashow more