Three challenges in finetuning Trellis
Finetuning Trellis can dramatically improve image-conditioned 3D mesh generation, but it is not exactly plug-and-play. Out-of-the-box settings quickly run into bottlenecks around preprocessing time, GPU memory and overfitting. Drawing from experience gained across Datameister projects, we outline where finetuning efforts tend to succeed or fail in practice. We show how data coherence, preprocessing choices, memory-aware training, and careful regularization shape the outcome far more than aggressive hyperparameter tuning.
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