Digital dance and theater archives are difficult to analyse and understand because few tools can accurately extract 3D motion from monocular images, which are often low-quality in heritage collections. We present two cloud-ready pipelines that transform low-quality videos into temporally dense SMPL-X reconstructions, per-frame segmentation masks, depth maps, and estimated camera trajectories. The PREMIERE pipeline targets archival material, while the MultiPerson pipeline is tuned for high-frame-rate smartphone or action camera recordings with up to five performers. Both pipelines couple Segment Anything v2.1, Neural Localizer Fields pose recovery, MoGe-based depth cues, WiLoR hand refinement, and VGGT for camera parameters estimation. This is followed by scale-aware optimization and RBF smoothing. Extensive testing on the AIST++ dance data set confirms that the resulting 3D assets surpass earlier monocular baselines in pose fidelity and temporal stability, while remaining robust to motion blur, extreme lighting, and human occlusion on stage film. Outputs conform to a unified geometry schema that feeds the Horizon PREMIERE project tools and two open-source WebGL viewers, enabling browser-based playback, VR/MR immersion, and high-resolution render export with no additional recording hardware. All codes, evaluation scripts, and viewers will be released under an open licence, offering a reproducible, extensible foundation for large-scale motion mining and the preservation of intangible cultural heritage. See interactive results on our project page: https://www.couleur.org/PREMIERE/JMTA/ .

Computational color constancy has seen strong improvement these last years due to the emergence of large labeled datasets. However, the models trained on images acquired by some cameras show low generalization power when tested on images acquired by other cameras. Indeed, since the light chromaticities are device dependent, the training distribution is very spread out when mixing different sensors. In this paper, we propose to inform the network that this complex distribution is a set of simpler distributions, one for each considered camera. For this purpose, we create a Siamese architecture trained with a specific contrastive loss. This loss enforces the model to predict light chromaticities in the same sensor distribution, when considering images acquired by the same camera and in different distributions for images from different sensors. The key idea consists in learning a specific color distance that is sensitive to only sensor variations and not to lighting variations. This learned distance is a nice tool to control if two chromaticity points are in the same sensor distribution or not. We test this original training process in the context of cross-camera color constancy and we show that it outperforms the alternatives on three datasets.

The aim of this paper is to present different algorithms, based on a combination of two structures of graph and of two color image processing methods, in order to segment color images. The structures used in this study are the region adjacency graph and the line graph associated.We will see how these structures can enhance segmentation processes such as region growing or watershed transformation. The principal advantage of these structures is that they give more weight to adjacency relationships between regions than usual methods. Let us note nevertheless that this advantage leads in return to adjust more parameters than other methods to best refine the result of the segmentation.We will show that this adjustment is necessarily image dependent and observer dependent.

We describe here a new method for object detection that uses a special structure called a pyramidal adjacency graph. Using that structure, which is built upon a Gaussian pyramid structure, and several predicates of color homogeneity, we are able to detect a spot region in a color pyramid. One interesting aspect of this structure is that it helps to describe connections between events at different levels of a pyramid, and so provides a more global analysis.