{HiFECap}: {M}onocular High-Fidelity and Expressive Capture of Human Performances

Monocular 3D human performance capture is indispensable for many applicationsin computer graphics and vision for enabling immersive experiences. However,detailed capture of humans requires tracking of multiple aspects, including theskeletal pose, the dynamic surface, which includes clothing, hand gestures aswell as facial expressions. No existing monocular method allows joint trackingof all these components. To this end, we propose HiFECap, a new neural humanperformance capture approach, which simultaneously captures human pose,clothing, facial expression, and hands just from a single RGB video. Wedemonstrate that our proposed network architecture, the carefully designedtraining strategy, and the tight integration of parametric face and hand modelsto a template mesh enable the capture of all these individual aspects.Importantly, our method also captures high-frequency details, such as deformingwrinkles on the clothes, better than the previous works. Furthermore, we showthat HiFECap outperforms the state-of-the-art human performance captureapproaches qualitatively and quantitatively while for the first time capturingall aspects of the human.<br

The cytoplasmic poly(A) polymerases GLD-2 and GLD-4 promote general gene expression via distinct mechanisms

Post-transcriptional gene regulation mechanisms decide on cellular mRNA activities. Essential gatekeepers of post-transcriptional mRNA regulation are broadly conserved mRNA-modifying enzymes, such as cytoplasmic poly(A) polymerases (cytoPAPs). Although these non-canonical nucleotidyltransferases efficiently elongate mRNA poly(A) tails in artificial tethering assays, we still know little about their global impact on poly(A) metabolism and their individual molecular roles in promoting protein production in organisms. Here, we use the animal model Caenorhabditis elegans to investigate the global mechanisms of two germline-enriched cytoPAPs, GLD-2 and GLD-4, by combining polysome profiling with RNA sequencing. Our analyses suggest that GLD-2 activity mediates mRNA stability of many translationally repressed mRNAs. This correlates with a general shortening of long poly(A) tails in gld-2-compromised animals, suggesting that most if not all targets are stabilized via robust GLD-2-mediated polyadenylation. By contrast, only mild polyadenylation defects are found in gld-4-compromised animals and few mRNAs change in abundance. Interestingly, we detect a reduced number of polysomes in gld-4 mutants and GLD-4 protein co-sediments with polysomes, which together suggest that GLD-4 might stimulate or maintain translation directly. Our combined data show that distinct cytoPAPs employ different RNA-regulatory mechanisms to promote gene expression, offering new insights into translational activation of mRNAs

Structural basis for the antagonistic roles of RNP-8 and GLD-3 in GLD-2 poly(A)-polymerase activity

Cytoplasmic polyadenylation drives the translational activation of specific mRNAs in early metazoan development and is performed by distinct complexes that share the same catalytic poly(A)-polymerase subunit, GLD-2. The activity and specificity of GLD-2 depend on its binding partners. In Caenorhabditis elegans, GLD-2 promotes spermatogenesis when bound to GLD-3 and oogenesis when bound to RNP-8. GLD-3 and RNP-8 antagonize each other and compete for GLD-2 binding. Following up on our previous mechanistic studies of GLD-2-GLD-3, we report here the 2.5 resolution structure and biochemical characterization of a GLD-2-RNP-8core complex. In the structure, RNP-8 embraces the poly(A)-polymerase, docking onto several conserved hydrophobic hotspots present on the GLD-2 surface. RNP-8 stabilizes GLD-2 and indirectly stimulates polyadenylation. RNP-8 has a different amino-acid sequence and structure as compared to GLD-3. Yet, it binds the same surfaces of GLD-2 by forming alternative interactions, rationalizing the remarkable versatility of GLD-2 complexes

Unbiased 4D: Monocular 4D Reconstruction with a Neural Deformation Model

Capturing general deforming scenes is crucial for many computer graphics andvision applications, and it is especially challenging when only a monocular RGBvideo of the scene is available. Competing methods assume dense point tracks,3D templates, large-scale training datasets, or only capture small-scaledeformations. In contrast to those, our method, Ub4D, makes none of theseassumptions while outperforming the previous state of the art in challengingscenarios. Our technique includes two new, in the context of non-rigid 3Dreconstruction, components, i.e., 1) A coordinate-based and implicit neuralrepresentation for non-rigid scenes, which enables an unbiased reconstructionof dynamic scenes, and 2) A novel dynamic scene flow loss, which enables thereconstruction of larger deformations. Results on our new dataset, which willbe made publicly available, demonstrate the clear improvement over the state ofthe art in terms of surface reconstruction accuracy and robustness to largedeformations. Visit the project page https://4dqv.mpi-inf.mpg.de/Ub4D/.<br