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HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling

About

Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques. However, the volume rendering procedures that drive these representations necessitate careful trade-offs in terms of quality, rendering speed, and memory efficiency. In particular, existing methods fail to simultaneously achieve real-time performance, small memory footprint, and high-quality rendering for challenging real-world scenes. To address these issues, we present HyperReel -- a novel 6-DoF video representation. The two core components of HyperReel are: (1) a ray-conditioned sample prediction network that enables high-fidelity, high frame rate rendering at high resolutions and (2) a compact and memory-efficient dynamic volume representation. Our 6-DoF video pipeline achieves the best performance compared to prior and contemporary approaches in terms of visual quality with small memory requirements, while also rendering at up to 18 frames-per-second at megapixel resolution without any custom CUDA code.

Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhoefer, Johannes Kopf, Matthew O'Toole, Changil Kim• 2023

Related benchmarks

TaskDatasetResultRank
Novel View SynthesisNeural 3D Video Dataset Standard (All six scenes)
PSNR31.1
36
Dynamic Scene ReconstructionN3DV (test)
PSNR31.1
32
Novel View SynthesisNeu3D (test)
PSNR31.1
18
Dynamic Scene ReconstructionNeural 3D Video 19 (full)
PSNR31.1
17
Dynamic View SynthesisNeural 3D Video 19 (test)
PSNR31.1
16
3D Video SynthesisNeural 3D Video Dataset (Cut Roasted Beef scene)
PSNR32.25
12
Novel View RenderingN3DV Cut Roast Beef
PSNR32.25
11
Novel View RenderingN3DV Cook Spinach
PSNR31.77
11
Novel View RenderingN3DV Sear Steak
PSNR31.88
11
Novel View RenderingN3DV Flame Steak
PSNR31.48
11
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