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arxiv: 2606.29333 · v1 · pith:OSMGTNWFnew · submitted 2026-06-28 · 💻 cs.CV

HiReFF: High-Resolution Feedforward Human Reconstruction from Uncalibrated Sparse-View Video

Yiming Jiang , Hanzhang Tu , Wenfeng Song , Siyou Lin , Liang An , Shuai Li , Aimin Hao , Yebin Liu This is my paper

Pith reviewed 2026-06-30 07:33 UTC · model grok-4.3

classification 💻 cs.CV
keywords human reconstruction3D Gaussianfeed-forwardsparse-view videohigh-resolution synthesisuncalibrated camerasvolumetric video
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The pith

HiReFF turns four uncalibrated 90-degree video views into 2K 360-degree human reconstructions in one forward pass.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents HiReFF as a feed-forward pipeline that reconstructs high-resolution human geometry and appearance from sparse uncalibrated video without per-scene optimization. It splits the work into first building clean foreground 3D Gaussians from four views spaced at 90 degrees, then adding high-resolution detail through a lightweight side network. Scale-synchronized Camera Calibration removes scale ambiguity so multi-view losses can supervise the Gaussians, while Gaussian-wise Foreground Masking keeps background artifacts out of the model. High-resolution Side-tuning then augments the low-resolution Gaussian backbone to reach 2K output at low extra cost. If these steps hold, the method makes real-time volumetric human streaming practical with ordinary camera setups.

Core claim

HiReFF is a feed-forward method for 2K-resolution 360° human video reconstruction from uncalibrated sparse-view videos that decomposes the task into foreground 3D Gaussian reconstruction using Scale-synchronized Camera Calibration and Gaussian-wise Foreground Masking, followed by High-resolution Side-tuning that augments the Gaussian head with supplementary features to achieve efficient 2K rendering while keeping the backbone at 0.5K.

What carries the argument

3D Gaussian representation with Scale-synchronized Camera Calibration to resolve scale for multi-view supervision, Gaussian-wise Foreground Masking to modulate parameters for clean foregrounds, and High-resolution Side-tuning to add detail for 2K output.

If this is right

  • Removes the requirement for calibrated camera rigs or per-scene optimization in high-resolution human volumetric video.
  • Delivers 2K rendering while the main network stays at 0.5K, cutting compute for streaming applications.
  • Produces temporally consistent reconstructions directly from video input rather than single frames.
  • Enables deployment on ordinary uncalibrated camera setups for AR/VR and holographic communication.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The side-tuning pattern could transfer to other sparse-view Gaussian reconstruction tasks beyond humans.
  • If the 90-degree spacing assumption is relaxed, the calibration step might need re-derivation for arbitrary camera placements.
  • Real-world use would still require handling fast motion or clothing deformation that the current four-view setup may not capture cleanly.

Load-bearing premise

Four views separated by 90 degrees together with the calibration and masking steps suffice to produce accurate clean foreground 3D Gaussians without per-scene optimization or extra constraints.

What would settle it

A test on held-out multi-view video where the 3D Gaussian output from exactly those four uncalibrated views is compared to ground-truth geometry; if surface error or visual quality falls below optimized per-scene baselines, the claim does not hold.

Figures

Figures reproduced from arXiv: 2606.29333 by Aimin Hao, Hanzhang Tu, Liang An, Shuai Li, Siyou Lin, Wenfeng Song, Yebin Liu, Yiming Jiang.

Figure 1
Figure 1. Figure 1: We present HiReFF, a feed-forward method for 2K-resolution 360° human video reconstruction from uncalibrated sparse-view videos. With four-view uncalibrated videos as input, HiReFF reconstructs a 360° human in a streaming fashion at 3.01 FPS on a single RTX 4090 GPU and achieves 2K resolution with only 34% additional VRAM during training compared to 0.5K. 1 Introduction Uncalibrated volumetric video stream… view at source ↗
Figure 2
Figure 2. Figure 2: Method Overview (§3). Taking four-view uncalibrated videos as input, we first extract features using an Alternating-Attention (AA) Transformer, then decode to obtain Gaussian parameters, supervising through rendered multi-view images. Specifi￾cally, HiReFF employs Scale-synchronized Camera Calibration (Sec. 3.2) to introduce supervision from additional viewpoints while indirectly supervising the Camera Hea… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results of novel-view synthesis of reconstruction [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: More visualization results. For both top-down and bottom-up perspectives, our method successfully reconstructs the correct geometry and accurately reproduces surface coloration. Please ü zoom in to see details. Time Fixed View Rendering Time t=0.0 t=0.2 t=0.4 t=0.6 t=0.8 t=1.0 t=1.2 t=1.4 t=1.6 t=1.8 t=2.0 t=0.0 t=0.2 t=0.4 t=0.6 t=0.8 t=1.0 t=1.2 t=1.4 t=1.6 t=1.8 Fixed View Rendering [PITH_FULL_IMAGE:fi… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on timing consistency. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Pre-trained prediction masks [29] occasionally exhibit imperfections, introduc￾ing challenges in metric evaluation. (§4.2). Please ü zoom in to see details. SSIM by 0.0124, and reduces LPIPS by 0.0460 compared to its unoptimized ver￾sion, while still maintaining clear advantages over its optimized variant. For GPS￾Gaussian [75], despite achieving comparable SSIM when provided with ground￾truth cameras and … view at source ↗
Figure 7
Figure 7. Figure 7: Ablation on Gaussian-wise Foreground Masking. Our method ef￾fectively removes the background. Input Views Novel Rendering Views [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
read the original abstract

Uncalibrated volumetric video streaming for human reconstruction is essential for holographic communication and AR/VR, yet remains challenging due to the need for temporal consistency and computational efficiency from sparse-view inputs. Existing methods rely on per-scene optimization or calibrated cameras, while recent feed-forward models are limited to low-resolution (0.5K) single-frame synthesis. We present HiReFF, a feed-forward method for 2K-resolution 360{\deg} human video reconstruction from uncalibrated sparse-view videos. Our framework decomposes the problem into two key tasks: foreground 3D Gaussian reconstruction from sparse-view videos (four views separated by 90{\deg}) and computationally efficient high-resolution synthesis. To enable the former, we propose Scale-synchronized Camera Calibration to resolve scale ambiguity for multi-view supervision, and Gaussian-wise Foreground Masking to reconstruct clean foregrounds by modulating Gaussian parameters. For efficient high-resolution synthesis, our High-resolution Side-tuning achieves 2K rendering by augmenting the Gaussian head with supplementary features while keeping the backbone at 0.5K, drastically reducing computational overhead. Experiments demonstrate that HiReFF significantly outperforms existing methods in high-resolution streaming volumetric video reconstruction. https://iridescentjiang.github.io/HiReFF

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents HiReFF, a feed-forward method for 2K-resolution 360° human video reconstruction from uncalibrated sparse-view videos (four views separated by 90°). It decomposes the task into foreground 3D Gaussian reconstruction via Scale-synchronized Camera Calibration (to resolve scale ambiguity) and Gaussian-wise Foreground Masking (to produce clean foregrounds), plus High-resolution Side-tuning to enable efficient 2K rendering by augmenting a 0.5K backbone. The abstract claims that experiments demonstrate significant outperformance over existing methods in high-resolution streaming volumetric video reconstruction.

Significance. If the central claims hold with supporting evidence, the work would advance feed-forward human reconstruction by enabling high-resolution output without per-scene optimization or calibrated cameras, with potential impact on AR/VR and holographic communication applications. The approach of side-tuning for resolution and the specific calibration/masking modules could address key efficiency and quality bottlenecks in sparse-view settings.

major comments (2)
  1. [Abstract] Abstract: the assertion that 'experiments demonstrate that HiReFF significantly outperforms existing methods in high-resolution streaming volumetric video reconstruction' supplies no metrics, datasets, baselines, implementation details, or quantitative results. This evidence gap is load-bearing for the central claim of outperformance and prevents verification of whether the proposed modules and 90° view configuration suffice for accurate clean foreground 3D Gaussians.
  2. [Method] The method description relies on the assumption that Scale-synchronized Camera Calibration and Gaussian-wise Foreground Masking, combined with four 90°-separated views, enable accurate feed-forward reconstruction without per-scene optimization; however, the absence of any reported validation (e.g., ablation studies or comparison tables) leaves this core assumption untested in the provided manuscript.
minor comments (1)
  1. [Abstract] The abstract and title use 'uncalibrated sparse-view videos' but the method specifies a fixed four-view 90° configuration; clarifying whether the approach generalizes beyond this setup would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for clearer evidence in the abstract and explicit validation of the core method assumptions. We address each point below and will revise the manuscript to strengthen these aspects.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that 'experiments demonstrate that HiReFF significantly outperforms existing methods in high-resolution streaming volumetric video reconstruction' supplies no metrics, datasets, baselines, implementation details, or quantitative results. This evidence gap is load-bearing for the central claim of outperformance and prevents verification of whether the proposed modules and 90° view configuration suffice for accurate clean foreground 3D Gaussians.

    Authors: We acknowledge that the abstract's brevity omits specific quantitative support for the outperformance claim. The full manuscript (Section 4) provides detailed comparisons on standard datasets against relevant baselines, reporting metrics including PSNR, SSIM, and perceptual scores at both 0.5K and 2K resolutions, along with implementation details. To address the concern, we will revise the abstract to concisely include key quantitative results supporting the claim while remaining within length constraints. revision: yes

  2. Referee: [Method] The method description relies on the assumption that Scale-synchronized Camera Calibration and Gaussian-wise Foreground Masking, combined with four 90°-separated views, enable accurate feed-forward reconstruction without per-scene optimization; however, the absence of any reported validation (e.g., ablation studies or comparison tables) leaves this core assumption untested in the provided manuscript.

    Authors: The method section describes the proposed components, with the validation of their effectiveness (including ablations on the calibration and masking modules, and comparisons under the 90° sparse-view setup) presented in the subsequent experiments section of the manuscript. If the provided review copy did not clearly link these, we will add explicit cross-references from the method to the corresponding ablation and comparison results to make the validation more immediate. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The abstract and described framework introduce two new modules (Scale-synchronized Camera Calibration and Gaussian-wise Foreground Masking) plus High-resolution Side-tuning as independent engineering contributions for feed-forward 3D Gaussian reconstruction. No equations, parameter fits, or derivations are shown that reduce by construction to the inputs; the central claim rests on experimental outperformance rather than self-referential fitting or self-citation chains. The method is presented as externally validated against prior work without load-bearing uniqueness theorems or ansatzes imported from the same authors.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities; full manuscript required for any audit.

pith-pipeline@v0.9.1-grok · 5779 in / 1134 out tokens · 32475 ms · 2026-06-30T07:33:17.111661+00:00 · methodology

discussion (0)

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Reference graph

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