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LARGE LANGUAGE MODELS ARE NOT FAIR EVALUATORS

It is challenging to compare the performance of multiple models to one another. Traditional automated measures aren't robust and human evaluation isn't scalable. Recently people have been turning to GPT4 as a proxy for human preference. After all, it was trained with RLHF. However, doing this naively results in miscalibration. This work proposes a few simple fixes that aligns GPT4 evaluations more closely with those of humans.

ENHANCING TEXT-TO-3D GENERATION WITH PROLIFICDREAMER

ProlificDreamer is a new method for improving text-to-3D generation. It solves problems like over-saturation and low-diversity found in the earlier Score Distillation Sampling (SDS) approach by treating the 3D parameter as a random variable. This Variational Score Distillation (VSD) framework can produce higher quality and more diverse results, allowing the creation of highly detailed and photo-realistic 3D images from text, including complex effects such as smoke and drops.

THREESTUDIO

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