16.04.2026, 19:34
Hi Dan,
This is really incredible work — what you’ve built with CMNET2 is seriously impressive.
The integration of XMem2-style memory, adaptive VRAM management, and the DINOv2 + ResNet50 fusion backbone makes the whole pipeline feel much more robust and production-ready. The sliding window memory approach is also a very smart addition for long sequences.
Your analysis of the two core problems is spot on. I completely agree — both issues are more related to temporal coverage and reference consistency than to feature extraction quality, so it makes sense that DINOv3 wouldn’t fundamentally solve them.
SAM3 could definitely add a very useful layer, especially for improving object-level consistency and controlling how colors propagate across frames. That direction sounds very promising.
I’m going to test the pipeline you’ve built and see how it performs in practice.
Great work, seriously — looking forward to seeing where you take this next.
Best,
Nass
Hi Dan,
I also wanted to mention that I’m available to help with SAM3, especially if you move toward training or experimentation.
I took a look at the GitHub earlier, and it’s really promising — the direction you’re taking makes a lot of sense for improving consistency and control in the pipeline.
If you need any support on that side, feel free to reach out.
Best,
Nass
Hi Dan,
I ran some tests, and the results with XMem2 are genuinely superior. There are no color jumps, the temporal consistency is very solid, and the integration of reference images works perfectly.
Really impressive work — congratulations.
I also have a strong feeling that SAM3 might do something really special here — maybe even a breakthrough.
Best,
Nass
This is really incredible work — what you’ve built with CMNET2 is seriously impressive.
The integration of XMem2-style memory, adaptive VRAM management, and the DINOv2 + ResNet50 fusion backbone makes the whole pipeline feel much more robust and production-ready. The sliding window memory approach is also a very smart addition for long sequences.
Your analysis of the two core problems is spot on. I completely agree — both issues are more related to temporal coverage and reference consistency than to feature extraction quality, so it makes sense that DINOv3 wouldn’t fundamentally solve them.
SAM3 could definitely add a very useful layer, especially for improving object-level consistency and controlling how colors propagate across frames. That direction sounds very promising.
I’m going to test the pipeline you’ve built and see how it performs in practice.
Great work, seriously — looking forward to seeing where you take this next.
Best,
Nass
Hi Dan,
I also wanted to mention that I’m available to help with SAM3, especially if you move toward training or experimentation.
I took a look at the GitHub earlier, and it’s really promising — the direction you’re taking makes a lot of sense for improving consistency and control in the pipeline.
If you need any support on that side, feel free to reach out.
Best,
Nass
Hi Dan,
I ran some tests, and the results with XMem2 are genuinely superior. There are no color jumps, the temporal consistency is very solid, and the integration of reference images works perfectly.
Really impressive work — congratulations.
I also have a strong feeling that SAM3 might do something really special here — maybe even a breakthrough.
Best,
Nass