Learning Controllable 3D Diffusion Models from Single-view Images

Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulties in acquiring 3D ground truth data for training. On the other hand, 3D GANs that integrate implicit 3D representations into GANs have shown remarkable 3D-aware generation when trained only on single-view image datasets. However, 3D GANs do not provide straightforward ways to precisely control image synthesis. To address these challenges, We present Control3Diff, a 3D diffusion model that combines the strengths of diffusion models and 3D GANs for versatile, controllable 3D-aware image synthesis for single-view datasets. Control3Diff explicitly models the underlying latent distribution (optionally conditioned on external inputs), thus enabling direct control during the diffusion process. Moreover, our approach is general and applicable to any type of controlling input, allowing us to train it with the same diffusion objective without any auxiliary supervision. We validate the efficacy of Control3Diff on standard image generation benchmarks, including FFHQ, AFHQ, and ShapeNet, using various conditioning inputs such as images, sketches, and text prompts. Please see the project website (\url{https://jiataogu.me/control3diff}) for video comparisons.Comment: work in progres

Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors

We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons. The learned representation can be used to synthesize novel view images of an arbitrary person from a sparse set of cameras, and further animate them with the user's pose control. While existing methods can either generalize to new persons or synthesize animations with user control, none of them can achieve both at the same time. We attribute this accomplishment to the employment of a 3D proxy for a shared multi-person human model, and further the warping of the spaces of different poses to a shared canonical pose space, in which we learn a neural field and predict the person- and pose-dependent deformations, as well as appearance with the features extracted from input images. To cope with the complexity of the large variations in body shapes, poses, and clothing deformations, we design our neural human model with disentangled geometry and appearance. Furthermore, we utilize the image features both at the spatial point and on the surface points of the 3D proxy for predicting person- and pose-dependent properties. Experiments show that our method significantly outperforms the state-of-the-arts on both tasks. The video and code are available at https://talegqz.github.io/neural_novel_actor

A Modified Shielding and Rapid Transition DDES Model for Separated Flows

In this paper, the major problems associated with detached eddy simulation (DES) (namely, modeled stress depletion (MSD) and slowing of the RANS to LES transition (RLT)) are discussed and reviewed, and relevant improvements are developed. A modified version for the delayed DES (DDES) method with adaptive modified adequate shielding and rapid transition is proposed; this is called MSRT DDES. The modified shielding strategy can be adjusted adaptively according to the local flow conditions: keeping the RANS behavior in the whole boundary layer when there is no resolved turbulence, and weakening the shielding function when resolved turbulence exists in the mainstream over the boundary layer. This strategy can significantly ameliorate the MSD in the RANS boundary layer, regardless of the mesh refinement, and avoid excessive shielding in the fully developed resolved turbulence that may otherwise delay the development of the separated and reattached flow. Three cases are designed to test the modified DDES, namely, complete shielding in the RANS zone of a boundary layer (the zero-pressure gradient turbulent boundary layer with the refined mesh), modified adaptive improved shielding with a rapid transition (the flow over a hump), and the overall performance in a complex 3D separation (the corner separation in a compressor cascade). The results show that the modified shielding function is more physical than earlier proposals compared to shielding functions, and according to detailed comparisons of the wall skin friction coefficients, velocity profiles, total pressure-loss coefficients, entropy production analyses, and so on, the MSD and RLT problems are moderately alleviated by the MSRT DDES

Phosphorylation-Coupled Proteolysis of the Transcription Factor MYC2 Is Important for Jasmonate-Signaled Plant Immunity

<div><p>As a master regulator of jasmonic acid (JA)–signaled plant immune responses, the basic helix-loop-helix (bHLH) Leu zipper transcription factor MYC2 differentially regulates different subsets of JA–responsive genes through distinct mechanisms. However, how MYC2 itself is regulated at the protein level remains unknown. Here, we show that proteolysis of MYC2 plays a positive role in regulating the transcription of its target genes. We discovered a 12-amino-acid element in the transcription activation domain (TAD) of MYC2 that is required for both the proteolysis and the transcriptional activity of MYC2. Interestingly, MYC2 phosphorylation at residue Thr328, which facilitates its turnover, is also required for the MYC2 function to regulate gene transcription. Together, these results reveal that phosphorylation-coupled turnover of MYC2 stimulates its transcription activity. Our results exemplify that, as with animals, plants employ an “activation by destruction” mechanism to fine-tune their transcriptome to adapt to their ever-changing environment.</p> </div

Phosphorylation of MYC2 at Thr328 Affects Its Turnover.

<p>(A) Total protein of <i>MYC2-4myc-15</i> plants was extracted and immunoprecipitated with an anti-myc antibody. Immunoprecipitated proteins were treated with alkaline phosphatase for 30 min then analyzed by western blotting using an anti-myc antibody. (B) <i>MYC2-4myc-15</i> transgenic plants were treated with or without 100 µM MeJA for 6 h. Total proteins containing equal amount of MYC2-4myc were loaded on a column that specifically binds phospho-proteins. Bound proteins were eluted and the amount of MYC2-4myc determined by western blotting with an anti-myc antibody. (C) Identification of MYC2 phosphorylation at Thr328 by mass spectrometry. Shown is a collision-induced dissociation mass spectrum of the phosphopeptide SIQFENGSSSTITENPNLDP(pT)PSPVHSQTQNPK (+3 charged, <i>m/z</i> 1212.22). The C-terminal fragments (<i>y</i> ions) are colored orange and the N-terminal fragments (<i>b</i> ions) are colored green. ^* and ^# indicate fragment ions with a neutral loss of phosphoric acid or H₂O, respectively. (D) Root growth inhibition assay of the transgenic plants as indicated. Seeds were germinated on 1/2 MS medium with or without 20 µM MeJA after 3 d stratification; photos were taken 8 d after germination. (E) <i>MYC2-4myc-10</i> and <i>MYC2^T328A-4myc-23</i> plants were treated for 6 h with 100 µM MeJA and/or 100 µM CHX. Total protein was analyzed by western blotting using an anti-myc antibody. Ponceau S staining of RbcS served as a loading control. (F) <i>MYC2-4myc-10</i> and <i>MYC2^T328A-4myc-23</i> plants were treated for 6 h with 100 µM MeJA and/or 50 µM MG132. Total protein was analyzed by western blotting using anti-myc antibody. Ponceau S staining of RbcS served as a loading control.</p