Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D Diffusion-based Editor

Recent years have witnessed considerable achievements in editing images with text instructions. When applying these editors to dynamic scene editing, the new-style scene tends to be temporally inconsistent due to the frame-by-frame nature of these 2D editors. To tackle this issue, we propose Control4D, a novel approach for high-fidelity and temporally consistent 4D portrait editing. Control4D is built upon an efficient 4D representation with a 2D diffusion-based editor. Instead of using direct supervisions from the editor, our method learns a 4D GAN from it and avoids the inconsistent supervision signals. Specifically, we employ a discriminator to learn the generation distribution based on the edited images and then update the generator with the discrimination signals. For more stable training, multi-level information is extracted from the edited images and used to facilitate the learning of the generator. Experimental results show that Control4D surpasses previous approaches and achieves more photo-realistic and consistent 4D editing performances. The link to our project website is https://control4darxiv.github.io.Comment: The link to our project website is https://control4darxiv.github.i

Effect of facial emotion recognition learning transfers across emotions

IntroductionPerceptual learning of facial expression is shown specific to the train expression, indicating separate encoding of the emotional contents in different expressions. However, little is known about the specificity of emotional recognition training with the visual search paradigm and the sensitivity of learning to near-threshold stimuli.MethodsIn the present study, we adopted a visual search paradigm to measure the recognition of facial expressions. In Experiment 1 (Exp1), Experiment 2 (Exp2), and Experiment 3 (Exp3), subjects were trained for 8 days to search for a target expression in an array of faces presented for 950 ms, 350 ms, and 50 ms, respectively. In Experiment 4 (Exp4), we trained subjects to search for a target of a triangle, and tested them with the task of facial expression search. Before and after the training, subjects were tested on the trained and untrained facial expressions which were presented for 950 ms, 650 ms, 350 ms, or 50 ms.ResultsThe results showed that training led to large improvements in the recognition of facial emotions only if the faces were presented long enough (Exp1: 85.89%; Exp2: 46.05%). Furthermore, the training effect could transfer to the untrained expression. However, when the faces were presented briefly (Exp3), the training effect was small (6.38%). In Exp4, the results indicated that the training effect could not transfer across categories.DiscussionOur findings revealed cross-emotion transfer for facial expression recognition training in a visual search task. In addition, learning hardly affects the recognition of near-threshold expressions

Data_Sheet_1_Effect of facial emotion recognition learning transfers across emotions.docx

IntroductionPerceptual learning of facial expression is shown specific to the train expression, indicating separate encoding of the emotional contents in different expressions. However, little is known about the specificity of emotional recognition training with the visual search paradigm and the sensitivity of learning to near-threshold stimuli.MethodsIn the present study, we adopted a visual search paradigm to measure the recognition of facial expressions. In Experiment 1 (Exp1), Experiment 2 (Exp2), and Experiment 3 (Exp3), subjects were trained for 8 days to search for a target expression in an array of faces presented for 950 ms, 350 ms, and 50 ms, respectively. In Experiment 4 (Exp4), we trained subjects to search for a target of a triangle, and tested them with the task of facial expression search. Before and after the training, subjects were tested on the trained and untrained facial expressions which were presented for 950 ms, 650 ms, 350 ms, or 50 ms.ResultsThe results showed that training led to large improvements in the recognition of facial emotions only if the faces were presented long enough (Exp1: 85.89%; Exp2: 46.05%). Furthermore, the training effect could transfer to the untrained expression. However, when the faces were presented briefly (Exp3), the training effect was small (6.38%). In Exp4, the results indicated that the training effect could not transfer across categories.DiscussionOur findings revealed cross-emotion transfer for facial expression recognition training in a visual search task. In addition, learning hardly affects the recognition of near-threshold expressions.</p