1. **The motivation is unclear.** From the introduction section, the main motivation to use Diffusion Probabilistic Field (DPF) is handling various modalities together with a unified model. As for the unified models, what I understand is a single model that can generate different modalities. However, from the description in the method and experiment sections, each modality has different coordinate-signal pairs and the models are trained for each modality separately. If so, such a method cannot be regarded as a unified framework in my view.
2. **Comparison with conventional diffusion models.** In Line 142-144, when comparing DPF with conventional diffusion models, the main difference is that DPF can be applied to sparse observation of fields. However, in the view-wise sampling subsection (Line 244), each time sample the tokens in n views, which is a dense modeling instead of sparse sampling.
3. **Comparison with DPF.** In my view, the main contribution of DPF is the context query pairs sampling and optimization. However, in Line 502, this paper mentions that the context query pairs are not used, which confuses me about the training objective in this paper. Does this paper use the diffusion optimization objective like epsilon-prediction or velocity-prediction? If so, the method is almost the same with DiT.
4. **Limited performance.** I do not see a part describing the dataset and hyperparameters used for training. So I assume the model is trained on each benchmark. If so, the performance is far from satisfactory since the compared methods are generalizable ones instead of fitting to a benchmark.
1. **The motivation is unclear.** From the introduction section, the main motivation to use Diffusion Probabilistic Field (DPF) is handling various modalities together with a unified model. As for the unified models, what I understand is a single model that can generate different modalities. However, from the description in the method and experiment sections, each modality has different coordinate-signal pairs and the models are trained for each modality separately. If so, such a method cannot be regarded as a unified framework in my view.