Translations:Diffusion Models Are Real-Time Game Engines/54/es: Difference between revisions

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'''Image Quality.''' We measure LPIPS (Zhang et al., [https://arxiv.org/html/2408.14837v1#bib.bib40 2018]) and PSNR using the teacher-forcing setup described in Section [https://arxiv.org/html/2408.14837v1#S2 2], where we sample an initial state and predict a single frame based on a trajectory of ground-truth past observations. When evaluated over a random holdout of 2048 trajectories taken in 5 different levels, our model achieves a PSNR of <math>29.43</math> and an LPIPS of <math>0.249</math>. The PSNR value is similar to lossy JPEG compression with quality settings of 20-30 (Petric & Milinkovic, [https://arxiv.org/html/2408.14837v1#bib.bib22 2018]). Figure [https://arxiv.org/html/2408.14837v1#S5.F5 5] shows examples of model predictions and the corresponding ground truth samples.

Calidad de imagen. Medimos LPIPS (Zhang et al., 2018) y PSNR utilizando la configuración de "teacher forcing" descrita en la sección 2, donde seleccionamos un estado inicial y predecimos un solo fotograma basado en una trayectoria de observaciones pasadas de referencia. Al evaluar sobre una muestra aleatoria de 2048 trayectorias tomadas en 5 niveles diferentes, nuestro modelo alcanza un PSNR de $29,43$ y un LPIPS de $0,249$ . El valor de PSNR es similar al de la compresión JPEG con pérdidas con ajustes de calidad de 20-30 (Petric & Milinkovic, 2018). La figura 5 muestra ejemplos de predicciones del modelo y las correspondientes muestras de referencia.