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arxiv: 2511.07940 · v2 · submitted 2025-11-11 · 💻 cs.CV

ISExplore:Informative Segment Selection for Efficient Personalized 3D Talking Face Generation

Rui-Qing Sun , Ang Li , Zhijing Wu , Tian Lan , Qianyu Lu , Xingshan Yao , Chen Xu , Xian-Ling Mao This is my paper

Pith reviewed 2026-05-18 00:05 UTC · model grok-4.3

classification 💻 cs.CV
keywords talking face generation3D Gaussian SplattingNeural Radiance Fieldssegment selectiondata efficiencypersonalized synthesisreference videoNeRF
0
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The pith

Carefully chosen short reference segments can match full video performance in personalized 3D talking face generation.

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

The paper asks whether minutes-long reference videos are truly required for high-quality personalized 3D talking face models built on NeRF or 3D Gaussian Splatting. It shows that a few seconds of well-chosen footage often produces results comparable to the full video. The authors introduce ISExplore to pick the best short segment automatically by measuring audio feature diversity, lip movement amplitude, and viewpoint diversity. Experiments confirm the approach cuts data processing and training time by more than five times while keeping generation fidelity. This shifts emphasis from collecting long recordings to selecting informative short ones.

Core claim

Our exploratory study reveals that a carefully selected reference segment of only a few seconds can often achieve performance comparable to that of using the full reference video. This finding suggests that the informativeness of reference data is more critical than its duration. Motivated by this observation, we propose ISExplore, a simple yet effective segment selection strategy that automatically identifies the most informative short reference segment based on three key data quality dimensions: audio feature diversity, lip movement amplitude, and viewpoint diversity. Extensive experiments demonstrate that ISExplore reduces data processing and training time by over 5x for both NeRF- and 3D

What carries the argument

ISExplore segment selection strategy, which scores candidate clips on audio feature diversity, lip movement amplitude, and viewpoint diversity to pick the single most informative few-second reference for training.

If this is right

  • Data preparation and model training time drop by a factor of more than five for both NeRF-based and 3DGS-based talking face methods.
  • High-fidelity personalized output is preserved despite using only a few seconds of reference instead of minutes.
  • The approach applies across different underlying 3D representations without changing the core generation pipeline.
  • Informativeness of the chosen reference matters more than raw duration for final synthesis quality.
  • The method offers a practical route to faster deployment of personalized talking face systems.

Where Pith is reading between the lines

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

  • The same selection logic could be tested on other personalized video tasks such as full-body motion transfer to see if short informative clips suffice there too.
  • Adding metrics for head pose variation or expression range might further improve segment choice on datasets with limited viewpoint change.
  • Integrating the selector into live capture pipelines could allow on-the-fly model fitting from brief user recordings.
  • The three dimensions may serve as a starting point for data-efficiency studies in related areas like neural avatar animation.

Load-bearing premise

That audio feature diversity, lip movement amplitude, and viewpoint diversity are the three key dimensions that sufficiently determine the informativeness of reference data for high-quality personalized TFG.

What would settle it

A controlled test on held-out speakers where the segment chosen by the three criteria produces visibly lower lip-sync accuracy or reconstruction quality than either a random same-length clip or the full reference video.

Figures

Figures reproduced from arXiv: 2511.07940 by Ang Li, Chen Xu, Qianyu Lu, Rui-Qing Sun, Tian Lan, Xian-Ling Mao, Xingshan Yao, Zhijing Wu.

Figure 1
Figure 1. Figure 1: The quality of the output videos of the models [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The relationship between different audio feature [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The relationship between audio feature diversity [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The workflow of our selection strategy. 5. Experiments 5.1. Dataset and Employ Details In our experiments, we collected eleven videos from previous work [15, 22, 27, 35] , following the standard experimental setup described in [17], with no identity overlap. The average frame rate of them is approximately 25 fps, with a central portrait in each frame. For audio feature extraction, we utilized the AVE modul… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results. We show the experimental [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Talking Face Generation (TFG) methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have recently achieved impressive progress in personalized talking head synthesis. However, existing methods typically require several minutes of reference video for meticulous preprocessing and fitting, resulting in hours of preparation time and limiting their practical applicability. In this paper, we revisit a fundamental yet underexplored question: do high-quality personalized TFG models truly require minutes-long reference videos? Our exploratory study reveals that a carefully selected reference segment of only a few seconds can often achieve performance comparable to that of using the full reference video. This finding suggests that the informativeness of reference data is more critical than its duration. Motivated by this observation, we propose ISExplore (Informative Segment Explore), a simple yet effective segment selection strategy that automatically identifies the most informative short reference segment based on three key data quality dimensions: audio feature diversity, lip movement amplitude, and viewpoint diversity. Extensive experiments demonstrate that ISExplore reduces data processing and training time by over 5x for both NeRF- and 3DGS-based methods, while preserving high-fidelity generation quality. Our method provides a practical and efficient solution for personalized TFG and offers new insights into data efficiency in 3D talking face generation.

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

3 major / 2 minor

Summary. The paper presents an exploratory study on personalized 3D talking face generation (TFG) with NeRF and 3DGS, claiming that a carefully selected reference segment of only a few seconds can achieve performance comparable to using the full minutes-long reference video. It proposes ISExplore, a heuristic that automatically selects the most informative short segment according to three dimensions (audio feature diversity, lip movement amplitude, and viewpoint diversity), yielding over 5x reductions in data processing and training time while preserving generation quality.

Significance. If the central empirical claim holds under more rigorous controls, the work would have clear practical significance for making personalized 3D TFG more accessible by reducing reference-video requirements from minutes to seconds. The paper earns credit for conducting extensive experiments across both NeRF- and 3DGS-based pipelines and for releasing what appears to be reproducible code and evaluation protocols. These elements strengthen the assessment of data-efficiency insights in the field.

major comments (3)
  1. [§3.2] §3.2 (ISExplore formulation): the claim that audio diversity, lip amplitude, and viewpoint diversity are jointly sufficient rests on an unablated assumption. No experiment shows that omitting any single dimension materially degrades downstream PSNR/LPIPS or lip-sync metrics on the fitted NeRF/3DGS model.
  2. [§4.3, Table 2] §4.3, Table 2: the central result that ISExplore-selected segments match full-video quality is not contrasted with randomly sampled segments of identical length. Without this control, it remains unclear whether the three hand-chosen proxies, rather than mere shortness, are responsible for the observed performance.
  3. [§5] §5 (experimental protocol): reported quality metrics lack error bars, multiple random seeds, or cross-subject statistical tests. This weakens the reliability of the “comparable performance” statement that underpins the 5× efficiency claim.
minor comments (2)
  1. [Figure 3] Figure 3 caption: the three diversity scores are plotted but the exact normalization and weighting used to combine them into a final informativeness score is not stated.
  2. [Abstract] Abstract: the phrase “over 5x” should be replaced by the precise average and range of speed-ups measured across the NeRF and 3DGS pipelines.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, making revisions where they strengthen the manuscript without misrepresenting our exploratory study.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (ISExplore formulation): the claim that audio diversity, lip amplitude, and viewpoint diversity are jointly sufficient rests on an unablated assumption. No experiment shows that omitting any single dimension materially degrades downstream PSNR/LPIPS or lip-sync metrics on the fitted NeRF/3DGS model.

    Authors: We agree that an explicit ablation isolating each dimension would provide stronger empirical support. The three criteria were selected based on domain knowledge from audio-visual analysis and 3D reconstruction, but we have now added ablation experiments in the revised Section 3.2 and supplementary material. These show that omitting any single dimension leads to measurable drops in the final PSNR, LPIPS, and lip-sync metrics for both NeRF and 3DGS pipelines. revision: yes

  2. Referee: [§4.3, Table 2] §4.3, Table 2: the central result that ISExplore-selected segments match full-video quality is not contrasted with randomly sampled segments of identical length. Without this control, it remains unclear whether the three hand-chosen proxies, rather than mere shortness, are responsible for the observed performance.

    Authors: This control is indeed necessary to attribute gains to the proposed criteria rather than segment length alone. We have added random-sampling baselines of identical duration to Table 2 and the corresponding discussion in Section 4.3. The updated results confirm that ISExplore outperforms random selection on quality metrics while retaining the reported efficiency gains. revision: yes

  3. Referee: [§5] §5 (experimental protocol): reported quality metrics lack error bars, multiple random seeds, or cross-subject statistical tests. This weakens the reliability of the “comparable performance” statement that underpins the 5× efficiency claim.

    Authors: We acknowledge the benefit of statistical reporting. In the revision we now report error bars computed from cross-subject variation and include a brief discussion of consistency across the evaluated subjects. Full multi-seed reruns for every NeRF and 3DGS fitting were not feasible given the computational cost; we have noted this practical limitation explicitly while emphasizing the consistent trends observed across two distinct backbones. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical heuristic validated by direct experiments

full rationale

The paper's core contribution is an empirical observation from exploratory experiments that short reference segments can match full-video performance, followed by a practical heuristic (ISExplore) that scores segments on three hand-chosen dimensions and selects the highest-scoring one. No derivation chain, equations, or first-principles claims are present that reduce to fitted parameters or self-referential definitions. The central result rests on comparative experiments rather than any load-bearing self-citation, ansatz smuggling, or renaming of known results. The method is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the three listed quality dimensions capture informativeness for TFG; no free parameters or invented entities are mentioned in the abstract.

axioms (1)
  • domain assumption Informativeness of reference video for personalized TFG is adequately measured by audio feature diversity, lip movement amplitude, and viewpoint diversity.
    This assumption directly motivates the ISExplore selection criteria and the claim that short segments suffice.

pith-pipeline@v0.9.0 · 5558 in / 1265 out tokens · 26046 ms · 2026-05-18T00:05:25.479110+00:00 · methodology

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