Implicit and Parametric Avatar Pose and Shape Estimation From a Single Frontal Image of a Clothed Human
Bibliographic record
Abstract
In this paper, we tackle the challenge of three-dimensional estimation of expressive, animatable, and textured human avatars from a single frontal image. Leveraging a Skinned Multi-Person Linear (SMPL) parametric body, we adjust the model parameters to faithfully reflect the shape and pose of the individual, relying on the mesh generated by a Pixel-aligned Implicit Function (PIFu) model. To robustly infer the SMPL parameters, we deploy a multi-step optimization process. Initially, we recover the position of 2D joints using an existing pose estimation tool. Subsequently, we utilize the 3D PIFu mesh together with the 2D pose to estimate the 3D position of joints. In the subsequent step, we adapt the body’s parametric model to the 3D joints through rigid alignment, optimizing for global translation and rotation. This step provides a robust initialization for further refinement of shape and pose parameters. The next step involves optimizing the pose and the first component of the SMPL shape parameters while imposing constraints to enhance model robustness. We then refine the SMPL model pose and shape parameters by adding two new registration loss terms to the optimization cost function: a point-to-surface distance and a Chamfer distance. Finally, we introduce a refinement process utilizing a deformation vector field applied to the SMPL mesh, enabling more faithful modeling of tight to loose clothing geometry. A notable advantage of our approach is the ability to generate detailed avatars with fewer vertices compared to previous research, enhancing computational efficiency while maintaining high fidelity. To complete our model, we design a texture extraction and completion approach. Our entirely automated approach was evaluated against recognized benchmarks, X-Avatar and PeopleSnapshot, showcasing competitive performance against state-of-the-art methods. This approach contributes to advancing 3D modeling techniques, particularly in the realms of interactive applications, animation, and video games. We will make the code accompanying our paper publicly available upon its acceptance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".