ArticleScientific reports2026
Transformer-based 3D pose estimation pipeline with modular SmoothNet integration for animation generation.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional 3D human pose estimation and animation generation methods still face high computational cost and temporal instability. To address these issues, this study develops a Transformer-based 3D pose estimation pipeline for spatial prediction and integrates SmoothNet as an external modular post-processing component for temporal smoothing. The backbone extracts image features with ResNet, encodes global dependencies using Transformer blocks, and outputs 3D keypoints for downstream reconstruction. SmoothNet is then applied to reduce frame-level jitter in predicted pose sequences. We evaluate the full pipeline on H3WB, AGORA, and AthletePose3D using MPJPE, PA-MPJPE, MPVE, and acceleration error. In addition, a fairness-controlled protocol on H3WB is introduced to compare MHFormer and the proposed backbone under unified SmoothNet post-processing settings, separating backbone effects from smoothing effects. Animation quality is further validated through Blender and Unreal Engine (UE) deployment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.