EchoWM for Interactive Multimodal World Simulation

# Illustrative sketch (not from the paper)
import torch
import torch.nn as nn

# Unified Camera Positional Encoding (UCPE)
class UCPE(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.linear = nn.Linear(6, dim)  # 6-DoF relative pose
    def forward(self, rel_pose):
        return self.linear(rel_pose)

# Core EchoWM model (backbone frozen after AV-CPT)
class EchoWM(nn.Module):
    def __init__(self, embed_dim=256):
        super().__init__()
        self.visual_encoder = nn.Identity()   # placeholder for pretrained visual/audio backbone
        self.ucpe = UCPE(embed_dim)
        self.action_head = nn.Linear(embed_dim, 6)  # predict next 6-DoF delta
    def forward(self, video_feat, rel_pose):
        # inject geometry into attention (simplified)
        pose_emb = self.ucpe(rel_pose)
        fused = video_feat + pose_emb
        return self.action_head(fused)

# Progressive curriculum (high‑level sketch)
model = EchoWM()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

# 1. AV‑CPT – pretrain backbone (omitted, assume frozen)
# 2. Action‑SFT – fine‑tune action head with frozen backbone
for epoch in range(2):  # illustrative short loop
    video_feat = torch.randn(8, 256)          # batch video embeddings
    rel_pose = torch.randn(8, 6)              # batch relative 6‑DoF commands
    pred = model(video_feat, rel_pose)
    loss = ((pred - rel_pose) ** 2).mean()   # trajectory sensitivity loss
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# 3. Joint‑FT – unfreeze all (omitted for brevity)
# 4. SGF autoregressive post‑training (placeholder)
print('Training sketch completed')

// Illustrative sketch (not from the paper)
const tf = require('@tensorflow/tfjs-node');

// Unified Camera Positional Encoding (UCPE)
function createUCPE(dim) {
  const input = tf.input({shape: [6]}); // 6‑DoF relative pose
  const dense = tf.layers.dense({units: dim, useBias: true}).apply(input);
  return tf.model({inputs: input, outputs: dense});
}

// Core EchoWM model (backbone frozen after AV‑CPT)
function createEchoWM(embedDim = 256) {
  const videoFeat = tf.input({shape: [embedDim]});
  const relPose = tf.input({shape: [6]});
  const ucpe = createUCPE(embedDim);
  const poseEmb = ucpe.apply(relPose);
  const fused = tf.layers.add().apply([videoFeat, poseEmb]);
  const actionHead = tf.layers.dense({units: 6}).apply(fused);
  return tf.model({inputs: [videoFeat, relPose], outputs: actionHead});
}

const model = createEchoWM();
const optimizer = tf.train.adam(1e-4);

// Action‑SFT loop (simplified)
async function trainStep() {
  const videoFeat = tf.randomNormal([8, 256]); // batch video embeddings
  const relPose = tf.randomNormal([8, 6]);    // batch relative 6‑DoF commands
  const lossFn = () => {
    const pred = model.predict([videoFeat, relPose]);
    const diff = tf.sub(pred, relPose);
    return tf.mean(tf.square(diff));
  };
  optimizer.minimize(lossFn);
}

(async () => {
  for (let epoch = 0; epoch < 2; epoch++) {
    await trainStep();
  }
  console.log('Training sketch completed');
})();