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| # DensePose CSE and DensePose Evolution | |
| * [DensePose Evolution pipeline](DENSEPOSE_IUV.md#ModelZooBootstrap), a framework to bootstrap | |
| DensePose on unlabeled data | |
| * [`InferenceBasedLoader`](../densepose/data/inference_based_loader.py) | |
| with data samplers to use inference results from one model | |
| to train another model (bootstrap); | |
| * [`VideoKeyframeDataset`](../densepose/data/video/video_keyframe_dataset.py) | |
| to efficiently load images from video keyframes; | |
| * Category maps and filters to combine annotations from different categories | |
| and train in a class-agnostic manner; | |
| * [Pretrained models](DENSEPOSE_IUV.md#ModelZooBootstrap) for DensePose estimation on chimpanzees; | |
| * DensePose head training from partial data (segmentation only); | |
| * [DensePose models with mask confidence estimation](DENSEPOSE_IUV.md#ModelZooMaskConfidence); | |
| * [DensePose Chimps]() dataset for IUV evaluation | |
| * [DensePose Continuous Surface Embeddings](DENSEPOSE_CSE.md), a framework to extend DensePose | |
| to various categories using 3D models | |
| * [Hard embedding](../densepose/modeling/losses/embed.py) and | |
| [soft embedding](../densepose/modeling/losses/soft_embed.py) | |
| losses to train universal positional embeddings; | |
| * [Embedder](../(densepose/modeling/cse/embedder.py) to handle | |
| mesh vertex embeddings; | |
| * [Storage](../densepose/evaluation/tensor_storage.py) for evaluation with high volumes of data; | |
| * [Pretrained models](DENSEPOSE_CSE.md#ModelZoo) for DensePose CSE estimation on humans and animals; | |
| * [DensePose Chimps](DENSEPOSE_DATASETS.md#densepose-chimps) and | |
| [DensePose LVIS](DENSEPOSE_DATASETS.md#densepose-lvis) datasets for CSE finetuning and evaluation; | |
| * [Vertex and texture mapping visualizers](../densepose/vis/densepose_outputs_vertex.py); | |
| * Refactoring of all major components: losses, predictors, model outputs, model results, visualizers; | |
| * Dedicated structures for [chart outputs](../densepose/structures/chart.py), | |
| [chart outputs with confidences](../densepose/structures/chart_confidence.py), | |
| [chart results](../densepose/structures/chart_result.py), | |
| [CSE outputs](../densepose/structures/cse.py); | |
| * Dedicated predictors for | |
| [chart-based estimation](../densepose/modeling/predictors/chart.py), | |
| [confidence estimation](../densepose/modeling/predictors/chart_confidence.py) | |
| and [CSE estimation](../densepose/modeling/predictors/cse.py); | |
| * Generic handling of various [conversions](../densepose/converters) (e.g. from outputs to results); | |
| * Better organization of various [losses](../densepose/modeling/losses); | |
| * Segregation of loss data accumulators for | |
| [IUV setting](../densepose/modeling/losses/utils.py) | |
| and [CSE setting](../densepose/modeling/losses/embed_utils.py); | |
| * Splitting visualizers into separate modules; | |
| * [HRNet](../densepose/modeling/hrnet.py) and [HRFPN](../densepose/modeling/hrfpn.py) backbones; | |
| * [PoseTrack](DENSEPOSE_DATASETS.md#densepose-posetrack) dataset; | |
| * [IUV texture visualizer](../densepose/vis/densepose_results_textures.py) | |