Hand Avatar Personalization Eval

hand-avatar-personalization-eval

PALM: A Dataset and Baseline for Learning Multi-subject Hand Prior — Fan et al. (2025) (arXiv:2511.05403, 2025)

What this evaluates

Evaluates the ability of a model to personalize a 3D hand avatar from a single RGB image and render it under novel poses and lighting conditions. It probes physically-based rendering accuracy, material/albedo recovery, and relighting generalization.

Datasets

• InterHand2.6M — total ?; splits: test (240)
• HARP relit — total ?; splits: train (-1), eval (-1)

Metrics

• PSNR (primary) — range: dB
  • Peak Signal-to-Noise Ratio, calculated as 10 * log10(MAX^2 / MSE) where MAX is the maximum pixel value (typically 255 for 8-bit images) and MSE is the mean squared error between predicted and ground truth images.
• SSIM — range: [0, 1]
  • Structural Similarity Index, measuring luminance, contrast, and structure between two images to assess perceptual similarity.
• LPIPS — range: [0, 1]
  • Learned Perceptual Image Patch Similarity, computed using a pre-trained deep network to measure perceptual distance between images.

Input / output format

Input: Single RGB image of a hand (used to train a per-sequence personalized model) and ground-truth reference images for evaluation.

Output: Rendered RGB image of the personalized hand avatar under a novel pose and/or environment lighting.

Scoring recipe

def evaluate(pred_rgb, gt_rgb):
    mse = np.mean((pred_rgb - gt_rgb) ** 2)
    psnr = 10 * np.log10(255**2 / mse)
    ssim = compute_ssim(pred_rgb, gt_rgb)
    lpips = compute_lpips(pred_rgb, gt_rgb)
    return {'PSNR': psnr, 'SSIM': ssim, 'LPIPS': lpips}

Common pitfalls

• Per-sequence personalization: The protocol requires training a separate personalized model for each sequence using only its first frame, rather than optimizing a single global model.
• Novel environment relighting: Evaluation explicitly uses different environment maps for training and testing, meaning models must generalize lighting rather than memorize it.
• Metric interpretation: LPIPS and SSIM are perceptual/structural metrics that may not perfectly align with visual fidelity in complex PBR materials, so qualitative inspection is recommended alongside quantitative scores.

Evidence (verbatim from paper)

We evaluate our baseline on the task of hand avatar personalization and relighting from a single RGB image using three different datasets. Metric and baseline details are in SupMat. Table 2: InterHand2.6M dataset evaluation. Comparison of methods on single-image personalization task using PSNR, SSIM, and LPIPS metrics.

Citation

@misc{fan2025palm,
  title={PALM: A Dataset and Baseline for Learning Multi-subject Hand Prior},
  author={Fan et al. (2025)},
  year={2025},
  note={arXiv:2511.05403}
}

• arXiv: 2511.05403