We present DreamHuman, a method to generate realistic animatable 3D human avatar models solely from textual descriptions.

In knowledge distillation, previous feature distillation methods mainly focus on the design of loss functions and the selection of the distilled layers, while the effectofthefeatureprojector between thestudent andtheteacher remains underexplored.

As an extension of traditional visual single object tracking (SOT) task [2, 3, 4], VLT can harness the complementary advantages of multiple modalities. Therefore, vision-language trackers (VLTs) have the potential to achieve more promising tracking performance, which has recently attracted widespreadattention[5,6,7,8].

However,maximum-marginclassifiers areinherently robusttoperturbations ofdata at prediction time, and this implication is at odds with concrete evidence that neural networks, in practice, are brittle toadversarial examples [71]and distribution shifts [52,58,44,65]. Hence, the linear setting, while convenient to analyze, is insufficient to capture the non-robustness of neural networkstrainedonrealdatasets.Goingbeyondthelinearsetting,severalworks[ 1,49,74]arguethat neuralnetworksgeneralize wellbecause standard training procedures haveabiastowardslearning