Comments (3)
一口吃胖子呢搁这,先 v 人 200 个 w
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@xiaoyuan1996 200万可不够哦,200亿美金才行。 去找中东的王子,他们钱多。
不一口吃个胖子,美其名曰超前一点,做的啥东西,都是在美国大公司和敏捷的创业公司后面跑。
当然,本质问题,还是视觉问题终极还是类似这种:动态立体交互场景。 就是UE中3D游戏,或者VR中的真立体游戏。
和人肉身在物理世界的视觉,就是双目下看世界一切。
way2实现相对容易一点,小规模的概念版本已经有了。 只是对象种类少,场景简单,分辨率低,双目有立体感但又错乱。 用个quest或者双目对对眼可以左右视频,可以私下分享看效果。
基于Latte修改,借鉴sora,扩展一下,实现个概念比较容易。 如果做立体,弄数据比较难。 我在动态立体模型上通过固定间隔的双摄像头采集的合成数据做的。没有钱采集海量双目数据,更没有大集群训练sora那种视觉效果。
现在:场景:独立运动的人物,长度:几帧几十帧; 双目立体视频。 稍后贴在这里。
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the first one in the world:
ai based dynamic/temporal stereo/binocular generation:
https://github.com/yuedajiong/super-ai/blob/main/superai-20240223-vision-dynamic-stereo-binocular.gif
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Related Issues (20)
- Any plan to implement Latte in HuggingFace's diffusers library? HOT 3
- 模型在ucf101上无法收敛 HOT 5
- Can Latte train for I2V tasks? HOT 2
- Batch Size Ablations HOT 1
- what the param <input_sq_size> stands for? HOT 2
- Can we use batch_size>1 in sample_t2x.py HOT 7
- Evaluate the `FVD` on FFS HOT 4
- How can I utilize the weights of pre-trained PixArt-α to initialize the parameters of the spatial Transformer block in the Latte T2V model? HOT 2
- FVD on UCF-101 HOT 15
- Is there tutorial on transfering t2i to t2v model? HOT 5
- inference memory with torch.set_grad_enabled(True) HOT 2
- T2V training and evaluation HOT 7
- Results & ckpts of different sized Latte on UCF-101 HOT 3
- 训练模型后的效果评估 HOT 4
- FFS evaluation HOT 5
- Some problems with reimplementing the training process HOT 20
- ImportError: cannot import name 'LattePipeline' HOT 3
- About training details HOT 2
- Works. But question: Attributes passed, but not expected (LatteTransformer3DModel) HOT 1
- About dataset of pretrained T2V model HOT 2
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