- The paper's main contribution is the design of a config-driven pipeline that standardizes heterogeneous sign language dataset preprocessing.
- It presents a modular, YAML-configurable workflow supporting both pose extraction and video cropping to optimize data usability and privacy.
- Empirical validation shows distinct impacts on throughput, landmark coverage, and model performance across different extraction backends.
SignDATA: Configurable Pipeline for Standardized Sign Language Dataset Preprocessing
Motivation and Context
Sign language translation (SLT) research is critically dependent on the availability and preprocessing of large sign language datasets, which are highly heterogeneous in annotation schema, recording conditions, clip timing, signer framing, and privacy constraints. While model architectures and downstream SLT performance have received significant attention, preprocessing pipelines remain fragmented, backend-specific, and under-documented, often resulting in inconsistent and non-reproducible input generation across studies. The paper introduces SignDATA (2604.20357), a config-driven, modular toolkit designed to standardize SLT dataset handling, manifest construction, landmark extraction, and output packaging across diverse corpora. By providing reproducible pipelines for both pose-based and signer-cropped video outputs, SignDATA explicitly foregrounds tradeoffs among extractor choice, normalization policy, privacy preservation, and experiment reproducibility.
System Architecture and Pipeline Design
SignDATA leverages a stage-based workflow parameterized via YAML configuration files, with registry-based architecture for dataset adapters, processors, and extraction backends. The pipeline is organized into four main stages:
- Dataset Acquisition: Source-specific adapters convert raw videos and metadata (e.g., transcripts, alignment files, split labels) into a canonical manifest schema, accommodating heterogeneous annotation formats and filtering by configurable criteria such as timing, required text, and duration bounds.
- Processing: Two complementary modes are supported. Pose mode extracts frame-wise landmark tensors using MediaPipe Holistic or MMPose backends, optionally following signer localization via YOLO or MMDetection. Video mode performs signer cropping and re-packages clips, using two-pass decoding to maximize usable clip rate in open-domain corpora.
- Post-Processing: Optional normalization, landmark reduction, flattening, or obfuscation policies are applied conditionally based on output representation, allowing explicit experimental tuning.
- Output Packaging: Processed pose arrays or cropped video clips, with aligned text and minimal metadata, are exported as WebDataset shards, suitable for high-throughput downstream model training and reproducible experiment management.
Run-scoped artifact directories and deterministic stage ordering further enforce isolation and reproducibility, while explicit backend interchangeability and manifest normalization facilitate consistent cross-dataset operations.
Technical Contributions and Empirical Validation
The core contribution is the reification of preprocessing as a structured systems problem, not an incidental implementation detail. By supporting both pose- and video-based outputs, and by exposing multiple extractor backends behind a unified interface, SignDATA makes normalization, privacy, and failure modes explicit and configurable rather than hidden. Empirical ablations reveal strong quantitative differences in throughput, landmark coverage, and usable-clip rate between MediaPipe and MMPose. Notably, pose extraction choices (such as depth coordinate retention or keypoint subset selection) produce measurable impacts on downstream model usability and storage footprint.
The pipeline’s modularity, reproducibility, backend interchangeability, dataset portability, and scalability are demonstrated through systematic backend comparisons, ablation experiments, and privacy-aware cropping in controlled and open-domain datasets (e.g., How2Sign, YouTube-ASL). SignDATA meets most design goals, with partial checkpoint integration and WebDataset export limiting unneeded reprocessing and streamlining large-scale experimentation.
Practical and Theoretical Implications
SignDATA advances infrastructure for SLT research, reducing repetitive engineering overhead and promoting reproducibility, especially for newcomers who may lack access to dataset-specific codebases. The explicit separation of dataset logic, processing steps, and extraction backends foregrounds the role of preprocessing decisions in model performance and broader research outcomes. By making privacy trade-offs empirically configurable, the toolkit supports responsible data handling and addresses recent critiques regarding biometric risk exposure in detailed facial landmarks, clarifying that pose-based representations are not inherently anonymizing [privacyslt2024].
Theoretical implications include the ability to systematize ablation studies on preprocessing parameters, enabling controlled experiments on normalization strategies, keypoint selection, and data augmentation. SignDATA’s extensibility and registry-driven design position it as a reusable layer for future SLT pipelines, including those targeting non-ASL corpora or multi-signer segmentation.
Limitations and Future Directions
SignDATA intentionally refrains from proposing novel pose estimators, focusing instead on infrastructure for robust SLT data preparation. Limitations include non-equivalence in landmark semantics across extraction backends—requiring researchers to commit to a backend prior to model input design. The current single-signer assumption excludes multi-person segment support, necessitating upstream filtering for open-domain datasets. WebDataset metadata is minimal, requiring post-hoc joining for users needing full manifest fields. Checkpoint utilities are partially integrated, leaving room for enhanced runtime stage-skipping and efficient restarts.
Future enhancements include integrating runtime checkpoints, developing privacy-preserving video obfuscation, and implementing cross-backend landmark ontology normalization to facilitate direct comparison and extractor switching. Expanding adapter coverage to additional sign language datasets remains an open extension.
Ethical Considerations
The paper foregrounds privacy concerns inherent to sign language video datasets, noting that signers are central to the communicative signal and thus subject to biometric risk. SignDATA supports privacy-aware cropping and pose extraction, but does not guarantee anonymization. Researchers are urged to respect dataset licensing and engage with Deaf communities in model development, acknowledging representational limitations and demographic biases in dataset coverage.
Conclusion
SignDATA (2604.20357) provides a reproducible, modular preprocessing framework for sign language translation datasets, transforming heterogeneous corpora into standardized pose tensors or signer-cropped video artifacts. By making backend choice, normalization, and privacy trade-offs explicit and empirically comparable, the toolkit offers a foundation for reproducible SLT research and cross-corpus benchmarking. SignDATA addresses a critical infrastructural gap, with several avenues for extension—including checkpointing, ontology alignment, and expanded dataset support—promising further advances in scalable and responsible sign language AI.