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(1/2) 📢📢📢NeRSemble #SIGGRAPH'23! We reconstruct dynamic radiance fields for high-quality novel view synthesis of human heads. Key is a deformation field and an ensemble of multi-resolution hash encodings to model coarse & fine-scale deformations.

Matthias Niessner

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69,596 görüntüleme • 3 yıl önce •via X (Twitter)

Eğitim Bilim & Teknoloji Haberler & Politika #SIGGRAPH

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Matthias Niessner profil fotoğrafı

Matthias Niessner3 yıl önce

(2/2) To train our model, we record a novel multi-view video dataset from 16 cameras containing over 4700 sequences of human heads covering a variety of facial expressions. The dataset will be publicly available to advance research on high-fidelity human head reconstruction.

Tobias Kirschstein profil fotoğrafı

Tobias Kirschstein2 yıl önce

Super excited to present NeRSemble at #SIGGRAPH2023 in the NeRFs for Avatars session happening tomorrow (Thursday) 3:45pm in Petree Hall C! Come by to chat about human-centric dynamic radiance fields and other face-related 3D topics!

Bobcat profil fotoğrafı

Bobcat3 yıl önce

That is just insane. You'll be outperforming ground truth in no time

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Progressively Optimized Local Radiance Fields for Robust View Synthesis paper page: present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, using a single, global radiance field with finite representational capacity does not scale to longer trajectories in an unbounded scene. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. We show that progressive optimization significantly improves the robustness of the reconstruction. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window. This further improves robustness (e.g., performs well even under moderate pose drifts) and allows us to scale to large scenes. Our extensive evaluation on the Tanks and Temples dataset and our collected outdoor dataset, Static Hikes, show that our approach compares favorably with the state-of-the-art.

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Andrea Tagliasacchi 🇨🇦

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📢📢📢Data release: high-res, multi-view, OLAT face recordings 📢📢📢 We captured individuals in our custom light stage with 16 high-end, global shutter cameras (72 fps) and 40 LED modules, totaling 2.8M precisely calibrated frames. We us the data for BecomingLit (#NeurIPS2025): intrinsically decomposed Gaussian avatars, enabling photorealistic and real-time relighting via hybrid neural shading. Code & Data: Great work by Jonathan Schmidt, Simon Giebenhain

Matthias Niessner

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MrNeRF

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LookCloser🔍: Frequency-aware Radiance Field for Tiny-Detail Scene TL;DR: FA-NeRF:l frequency-aware framework for view synthesis that simultaneously captures the overall scene structure and high-definition details within a single NeRF model.

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