MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation

We introduce MVControl, a novel neural network architecture that enhances existing pre-trained multi-view 2D diffusion models by incorporating additional input conditions, e.g. edge maps. Our approach enables the generation of controllable multi-view images and view-consistent 3D content. To achieve controllable multi-view image generation, we leverage MVDream as our base model, and train a new neural network module as additional plugin for end-to-end task-specific condition learning. To precisely control the shapes and views of generated images, we innovatively propose a new conditioning mechanism that predicts an embedding encapsulating the input spatial and view conditions, which is then injected to the network globally. Once MVControl is trained, score-distillation (SDS) loss based optimization can be performed to generate 3D content, in which process we propose to use a hybrid diffusion prior. The hybrid prior relies on a pre-trained Stable-Diffusion network and our trained MVControl for additional guidance. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content. Code available at https://github.com/WU-CVGL/MVControl/.Comment: Project page: https://lizhiqi49.github.io/MVControl

USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields

Neural Radiance Fields (NeRF) has received much attention recently due to its impressive capability to represent 3D scene and synthesize novel view images. Existing works usually assume that the input images are captured by a global shutter camera. Thus, rolling shutter (RS) images cannot be trivially applied to an off-the-shelf NeRF algorithm for novel view synthesis. Rolling shutter effect would also affect the accuracy of the camera pose estimation (e.g. via COLMAP), which further prevents the success of NeRF algorithm with RS images. In this paper, we propose Unrolling Shutter Bundle Adjusted Neural Radiance Fields (USB-NeRF). USB-NeRF is able to correct rolling shutter distortions and recover accurate camera motion trajectory simultaneously under the framework of NeRF, by modeling the physical image formation process of a RS camera. Experimental results demonstrate that USB-NeRF achieves better performance compared to prior works, in terms of RS effect removal, novel view image synthesis as well as camera motion estimation. Furthermore, our algorithm can also be used to recover high-fidelity high frame-rate global shutter video from a sequence of RS images

Ultrasmall amorphous zirconia nanoparticles catalyse polyolefin hydrogenolysis

Carbon–carbon bond cleavage reactions, adapted to deconstruct aliphatic hydrocarbon polymers and recover the intrinsic energy and carbon value in plastic waste, have typically been catalysed by metal nanoparticles or air-sensitive organometallics. Metal oxides that serve as supports for these catalysts are typically considered to be inert. Here we show that Earth-abundant, non-reducible zirconia catalyses the hydrogenolysis of polyolefins with activity rivalling that of precious metal nanoparticles. To harness this unusual reactivity, our catalytic architecture localizes ultrasmall amorphous zirconia nanoparticles between two fused platelets of mesoporous silica. Macromolecules translocate from bulk through radial mesopores to the highly active zirconia particles, where the chains undergo selective hydrogenolytic cleavage into a narrow, C18-centred distribution. Calculations indicated that C–H bond heterolysis across a Zr–O bond of a Zr(O)2 adatom model for unsaturated surface sites gives a zirconium hydrocarbyl, which cleaves a C–C bond via β-alkyl elimination.This article is published as Chen, Shaojiang, Akalanka Tennakoon, Kyung-Eun You, Alexander L. Paterson, Ryan Yappert, Selim Alayoglu, Lingzhe Fang et al. "Ultrasmall amorphous zirconia nanoparticles catalyse polyolefin hydrogenolysis." Nature Catalysis (2023): 1-13. DOI: 10.1038/s41929-023-00910-x. Copyright 2023 The Author(s). Attribution 4.0 International (CC BY 4.0). Posted with permission. DOE Contract Number(s): AC02-07CH11358; AC-02-06CH11357; AC02-05CH11231; CHE-2108306; TG-CTS090100

The first release of the AST3-1 Point Source Catalogue from Dome A, Antarctica

International audiencehe three Antarctic Survey Telescopes (AST3) aim to carry out time-domain imaging survey at Dome A, Antarctica. The first of the three telescopes (AST3-1) was successfully deployed in 2012 January. AST3-1 is a 500 mm aperture modified Schmidt telescope with a 680 mm diameter primary mirror. AST3-1 is equipped with a SDSS i filter and a 10k × 10k frame transfer CCD camera, reduced to 5k × 10k by electronic shuttering, resulting in a 4.3 deg2 field of view. To verify the capability of AST3-1 for a variety of science goals, extensive commissioning was carried out between 2012 March and May. The commissioning included a survey covering 2000 deg2 as well as the entire Large and Small Magellanic Clouds. Frequent repeated images were made of the centre of the Large Magellanic Cloud, a selected exoplanet transit field, and fields including some Wolf–Rayet stars. Here, we present the data reduction and photometric measurements of the point sources observed by AST3-1. We have achieved a survey depth of 19.3 mag in 60 s exposures with 5 mmag precision in the light curves of bright stars. The facility achieves sub-mmag photometric precision under stable survey conditions, approaching its photon noise limit. These results demonstrate that AST3-1 at Dome A is extraordinarily competitive in time-domain astronomy, including both quick searches for faint transients and the detection of tiny transit signal