PMMTalk: Speech-Driven 3D Facial Animation from Complementary Pseudo Multi-modal Features

Speech-driven 3D facial animation has improved a lot recently while most related works only utilize acoustic modality and neglect the influence of visual and textual cues, leading to unsatisfactory results in terms of precision and coherence. We argue that visual and textual cues are not trivial information. Therefore, we present a novel framework, namely PMMTalk, using complementary Pseudo Multi-Modal features for improving the accuracy of facial animation. The framework entails three modules: PMMTalk encoder, cross-modal alignment module, and PMMTalk decoder. Specifically, the PMMTalk encoder employs the off-the-shelf talking head generation architecture and speech recognition technology to extract visual and textual information from speech, respectively. Subsequently, the cross-modal alignment module aligns the audio-image-text features at temporal and semantic levels. Then PMMTalk decoder is employed to predict lip-syncing facial blendshape coefficients. Contrary to prior methods, PMMTalk only requires an additional random reference face image but yields more accurate results. Additionally, it is artist-friendly as it seamlessly integrates into standard animation production workflows by introducing facial blendshape coefficients. Finally, given the scarcity of 3D talking face datasets, we introduce a large-scale 3D Chinese Audio-Visual Facial Animation (3D-CAVFA) dataset. Extensive experiments and user studies show that our approach outperforms the state of the art. We recommend watching the supplementary video

Effect of Myostatin Depletion on Weight Gain, Hyperglycemia, and Hepatic Steatosis during Five Months of High-Fat Feeding in Mice

The marked hypermuscularity in mice with constitutive myostatin deficiency reduces fat accumulation and hyperglycemia induced by high-fat feeding, but it is unclear whether the smaller increase in muscle mass caused by postdevelopmental loss of myostatin activity has beneficial metabolic effects during high-fat feeding. We therefore examined how postdevelopmental myostatin knockout influenced effects of high-fat feeding. Male mice with ubiquitous expression of tamoxifen-inducible Cre recombinase were fed tamoxifen for 2 weeks at 4 months of age. This depleted myostatin in mice with floxed myostatin genes, but not in control mice with normal myostatin genes. Some mice were fed a high-fat diet (60% of energy) for 22 weeks, starting 2 weeks after cessation of tamoxifen feeding. Myostatin depletion increased skeletal muscle mass ∼30%. Hypermuscular mice had ∼50% less weight gain than control mice over the first 8 weeks of high-fat feeding. During the subsequent 3 months of high-fat feeding, additional weight gain was similar in control and myostatin-deficient mice. After 5 months of high-fat feeding, the mass of epididymal and retroperitoneal fat pads was similar in control and myostatin-deficient mice even though myostatin depletion reduced the weight gain attributable to the high-fat diet (mean weight with high-fat diet minus mean weight with low-fat diet: 19.9 g in control mice, 14.1 g in myostatin-deficient mice). Myostatin depletion did not alter fasting blood glucose levels after 3 or 5 months of high-fat feeding, but reduced glucose levels measured 90 min after intraperitoneal glucose injection. Myostatin depletion also attenuated hepatic steatosis and accumulation of fat in muscle tissue. We conclude that blocking myostatin signaling after maturity can attenuate some of the adverse effects of a high-fat diet

Current research status and development trends of fault sealing

The controlling effect of faults on hydrocarbon pool formation is mainly reflected in their impact on the processes of hydrocarbon migration, accumulation and distribution. Its essence is the problem of fault sealing. Fault sealing is typically influenced by a variety of factors, and the mechanism and the main controlling factors of fault sealing differ significantly across different layers, regions and geological periods. At present, there is no complete research system for the evaluation of fault sealing, and the accuracy of its evaluation needs to be improved. By comprehensively and systematically investigating recent research hotspots in the area of fault sealing, we summarize the mechanism of fault sealing, analyze the main controlling factors, systematically categorize the evaluation methods, and discuss the practical issues faced in fault sealing research. We also propose the development trends of future research. Fault sealing mechanisms can be divided into vertical and lateral sealing mechanisms. The former includes fault surface sealing and displacement pressure difference sealing within fault zones, while the latter includes sand and mud juxtaposition sealing, lateral sealing formed by shale smearing, and high displacement pressure sealing within fault zones. The main factors affecting fault sealing include fault development characteristics, lithology of the two fault walls, stress field environment, and diagenetic processes such as compaction, cementation, and dissolution. Different factors influence fault sealing in various ways, and fault sealing varies significantly with location and time. The research methods for fault sealing evaluation can be classified into four categories: (1) traditional geological methods: including qualitative and semi-quantitative analyses; (2) mathematical geological methods: including logistic information method, nonlinear mapping analysis, fuzzy comprehensive evaluation, grey correlation analysis, etc.; (3) numerical simulation of the tectonic stress field and calculation of parameters related to fault sealing; and (4) geochemical methods. Future research directions include fault opening and sealing mechanism and sealing evaluation of carbonate rock strata, the impact mechanism of stress and fluid coupling on fault sealing, comprehensive quantitative evaluations of fault sealing with multiple factors, the temporal and spatial evolution of fault sealing, and the assessment of fault connectivity

Mean (+SEM) muscle and intra-abdominal adipose tissue mass.

<p>Myostatin-deficient (gray bars) and control mice (white bars) were fed a normal low-fat diet (13% of energy from fat) or were fed a high-fat diet (60% of energy from fat) for the final 22 weeks of the experiment. Each bar represents the mean and SEM of 12–15 mice. *<i>P</i><0.001 versus mice with normal myostatin levels.</p

Hepatic steatosis scores and representative micrographs of liver sections.

<p>Distribution of steatosis scores (A) is based on examination of 12 mice with normal myostatin expression and 13 myostatin-deficient mice, all of which received the high-fat diet. Mice fed a low-fat diet did not have hepatic fat accumulation (B, Osmium H&E×250). Mice with normal myostatin expression had significant hepatic steatosis after 5 months of high-fat feeding (C×250; D×500). Larger lipid droplets often lift off the tissue leaving the clear spaces seen in the micrographs. Less fat accumulation was evident in livers of myostatin-deficient mice fed a high-fat diet for 5 months (E×250; F×500).</p

Effects of myostatin depletion and high-fat feeding on body and organ mass.

<p>Values are mean±standard error. Organ mass was determined when mice were 10 months old. Myostatin depletion was induced by tamoxifen feeding for two weeks when mice were 4 months old. High-fat feeding started when mice were 5 months old.</p>a<p><i>P</i><0.02 versus normal myostatin group on same diet.</p>b<p><i>P</i><0.03 versus low-fat diet group with same myostatin status.</p

Mean (±SEM) change in total body mass.

<p>Top panel shows cumulative weight gains after changing dietary fat from 13% to 60% of energy, and lower panel shows biweekly weight changes independent of previous measures. IPGTT = intraperitoneal glucose tolerance test. *<i>P</i><0.05 (adjusted for multiple comparisons) for difference between normal and myostatin-depleted.</p

Mean (+SEM) blood glucose concentrations.

<p>Bars represent means, whiskers represent SEM. Number of values included in each mean are shown at the bottom of each bar. *<i>P</i><0.01 vs. normal myostatin group represented by adjacent bar. #<i>P</i><0.02 vs. low-fat group with same myostatin status.</p