diff --git a/src/lerobot/datasets/depth_utils.py b/src/lerobot/datasets/depth_utils.py
index 0fd9e624b..cbded58de 100644
--- a/src/lerobot/datasets/depth_utils.py
+++ b/src/lerobot/datasets/depth_utils.py
@@ -20,6 +20,7 @@ Depth encoding/decoding helpers for :class:`VideoEncoderConfig`.
 import math
 from typing import Literal
 
+import av
 import numpy as np
 import torch
 from numpy.typing import NDArray
@@ -32,9 +33,14 @@ from lerobot.configs.video import (
     DEPTH_QMAX,
 )
 
+from .pyav_utils import write_u16_plane
+
 _MM_PER_METRE = 1000.0
 _UINT16_MAX = 65535
 
+# Pixel format supported by the depth encode/decode helpers.
+DEPTH_PIX_FMT: str = "gray12le"
+
 
 def _validate_log_quant_params(depth_min: float, shift: float) -> None:
     """Ensure ``log(depth_min + shift)`` is finite."""
@@ -46,33 +52,25 @@ def _validate_log_quant_params(depth_min: float, shift: float) -> None:
 
 
 def _depth_input_to_float32_and_unit(
-    depth: NDArray[np.uint16] | NDArray[np.floating] | torch.Tensor,
+    depth: NDArray[np.integer] | NDArray[np.floating],
     input_unit: Literal["auto", "m", "mm"],
 ) -> tuple[NDArray[np.float32], Literal["m", "mm"]]:
-    """Depth as float32 in the chosen unit, plus the resolved unit."""
-    if isinstance(depth, torch.Tensor):
-        t = depth.detach().cpu()
-        arr = t.numpy()
-        is_floating = t.is_floating_point()
-    else:
-        arr = np.asarray(depth)
-        is_floating = np.issubdtype(arr.dtype, np.floating)
-
-    resolved_unit = ("m" if is_floating else "mm") if input_unit == "auto" else input_unit
-
-    # Convert to float32 to keep typing consistency
-    return np.asarray(arr, dtype=np.float32, order="K"), resolved_unit
+    """Convert depth to float32 in the chosen unit, and return the resolved unit."""
+    resolved_unit = (
+        ("m" if np.issubdtype(depth.dtype, np.floating) else "mm") if input_unit == "auto" else input_unit
+    )
+    return depth.astype(np.float32, order="K"), resolved_unit
 
 
 def quantize_depth(
-    depth: NDArray[np.uint16] | NDArray[np.floating] | torch.Tensor,
+    depth: NDArray[np.uint16] | NDArray[np.float32] | torch.Tensor,
     depth_min: float = DEFAULT_DEPTH_MIN,
     depth_max: float = DEFAULT_DEPTH_MAX,
     shift: float = DEFAULT_DEPTH_SHIFT,
     use_log: bool = DEFAULT_DEPTH_USE_LOG,
-    *,
+    video_backend: str | None = "pyav",
     input_unit: Literal["auto", "m", "mm"] = "auto",
-) -> NDArray[np.uint16]:
+) -> NDArray[np.uint16] | av.VideoFrame:
     """Quantize depth to 12-bit codes (``uint16``, values ``0…DEPTH_QMAX``).
 
     Depth maps are packed into 12-bit integer frames so they fit in standard
@@ -98,6 +96,7 @@ def quantize_depth(
         depth_max: Depth (metres) at quantum :data:`DEPTH_QMAX`.
         shift: Depth shift (metres); used in log mode. Must satisfy ``depth_min + shift > 0``.
         use_log: If ``True`` (default), quantize in log space.
+        video_backend: Video backend to use for encoding. Defaults to "pyav".
         input_unit: Input unit policy (``"auto"``, ``"mm"``, ``"m"``).
 
     Returns:
@@ -111,11 +110,17 @@ def quantize_depth(
     if input_unit not in ("auto", "m", "mm"):
         raise ValueError(f"input_unit must be 'auto', 'm', or 'mm', got {input_unit!r}")
 
+    if isinstance(depth, torch.Tensor):
+        depth = depth.detach().cpu().numpy()
+
     depth_f, resolved_unit = _depth_input_to_float32_and_unit(depth, input_unit=input_unit)
+
+    # Convert depth_min, depth_max, and shift to the resolved input unit.
     depth_min_u = np.float32(depth_min) if resolved_unit == "m" else np.float32(depth_min * _MM_PER_METRE)
     depth_max_u = np.float32(depth_max) if resolved_unit == "m" else np.float32(depth_max * _MM_PER_METRE)
     shift_u = np.float32(shift) if resolved_unit == "m" else np.float32(shift * _MM_PER_METRE)
 
+    # Normalization and quantization is performed in the resolved input unit.
     if use_log:
         _validate_log_quant_params(depth_min, shift)
         log_min = math.log(float(depth_min_u + shift_u))
@@ -124,19 +129,25 @@ def quantize_depth(
     else:
         norm = (depth_f - depth_min_u) / (depth_max_u - depth_min_u)
 
-    out = np.rint(norm * DEPTH_QMAX).clip(0, DEPTH_QMAX)
-    return out.astype(np.uint16, copy=False)
+    quantized = np.rint(norm * DEPTH_QMAX).clip(0, DEPTH_QMAX).astype(np.uint16, copy=False)
+
+    if video_backend == "pyav":
+        frame = av.VideoFrame.from_ndarray(quantized, format=DEPTH_PIX_FMT)
+        write_u16_plane(frame.planes[0], quantized)
+        return frame
+    else:
+        return quantized
 
 
 def dequantize_depth(
-    quantized: NDArray[np.uint16] | torch.Tensor,
+    quantized: NDArray[np.uint16] | av.VideoFrame,
     depth_min: float = DEFAULT_DEPTH_MIN,
     depth_max: float = DEFAULT_DEPTH_MAX,
     shift: float = DEFAULT_DEPTH_SHIFT,
     use_log: bool = DEFAULT_DEPTH_USE_LOG,
-    *,
     output_unit: Literal["m", "mm"] = "mm",
-) -> NDArray[np.uint16] | NDArray[np.float32]:
+    output_tensor: bool = False,
+) -> NDArray[np.uint16] | NDArray[np.float32] | torch.Tensor:
     """Inverse of :func:`quantize_depth`.
 
     Tuning arguments **must match** :func:`quantize_depth`.
@@ -151,6 +162,7 @@ def dequantize_depth(
         output_unit: ``\"mm\"`` returns ``uint16`` millimetres (``rint``, clip
             ``[0, 65535]``). ``\"m\"`` returns ``float32`` metres in
             ``[depth_min, depth_max]``.
+        output_tensor: If True, return a torch.Tensor instead of a numpy array.
 
     Returns:
         Depth map in the requested unit and dtype.
@@ -162,25 +174,33 @@ def dequantize_depth(
     if output_unit not in ("m", "mm"):
         raise ValueError(f"output_unit must be 'm' or 'mm', got {output_unit!r}")
 
-    if isinstance(quantized, torch.Tensor):
-        quantized = quantized.detach().cpu().numpy()
-    q = np.asarray(quantized, dtype=np.uint16, order="K")
-    norm = q.astype(np.float32, copy=False) / DEPTH_QMAX
+    if isinstance(quantized, av.VideoFrame):
+        quantized = quantized.to_ndarray(format=DEPTH_PIX_FMT)
 
-    depth_min_mm = np.float32(depth_min * _MM_PER_METRE)
-    depth_max_mm = np.float32(depth_max * _MM_PER_METRE)
-    shift_mm = np.float32(shift * _MM_PER_METRE)
+    norm = np.asarray(quantized, dtype=np.float32, order="K") / DEPTH_QMAX
 
+    depth_min_m = np.float32(depth_min)
+    depth_max_m = np.float32(depth_max)
+    shift_m = np.float32(shift)
+
+    # The de-normalization and de-quantization is performed in meters (convenience choice).
     if use_log:
         _validate_log_quant_params(depth_min, shift)
-        log_min = math.log(float(depth_min_mm + shift_mm))
-        log_max = math.log(float(depth_max_mm + shift_mm))
-        depth_mm = np.exp(norm * (log_max - log_min) + log_min) - shift_mm
+        log_min = math.log(float(depth_min_m + shift_m))
+        log_max = math.log(float(depth_max_m + shift_m))
+        depth_m = np.exp(norm * (log_max - log_min) + log_min) - shift_m
     else:
-        depth_mm = norm * (depth_max_mm - depth_min_mm) + depth_min_mm
+        depth_m = norm * (depth_max_m - depth_min_m) + depth_min_m
+    depth_m = np.clip(depth_m, depth_min_m, depth_max_m).astype(np.float32, copy=False)
 
-    depth_mm = np.clip(depth_mm, depth_min_mm, depth_max_mm).astype(np.float32, copy=False)
+    # Return depth as float32 meters.
     if output_unit == "m":
-        return (depth_mm / np.float32(_MM_PER_METRE)).astype(np.float32, copy=False)
-    mm = np.rint(depth_mm).clip(0, _UINT16_MAX)
-    return mm.astype(np.uint16, copy=False)
+        return torch.from_numpy(depth_m) if output_tensor else depth_m
+
+    # Return depth as uint16 millimeters.
+    mm = np.rint(depth_m * _MM_PER_METRE).clip(0, _UINT16_MAX).astype(np.uint16, copy=False)
+    if output_tensor:
+        # torch.uint16 support is very limited, we convert to float32 instead.
+        return torch.from_numpy(mm.astype(np.float32))
+    else:
+        return mm
diff --git a/src/lerobot/datasets/pyav_utils.py b/src/lerobot/datasets/pyav_utils.py
index d291f8b40..1fbbe5f89 100644
--- a/src/lerobot/datasets/pyav_utils.py
+++ b/src/lerobot/datasets/pyav_utils.py
@@ -24,6 +24,7 @@ import logging
 from typing import Any
 
 import av
+import numpy as np
 
 logger = logging.getLogger(__name__)
 
@@ -31,6 +32,16 @@ FFMPEG_NUMERIC_OPTION_TYPES = ("INT", "INT64", "UINT64", "FLOAT", "DOUBLE")
 FFMPEG_INTEGER_OPTION_TYPES = ("INT", "INT64", "UINT64")
 
 
+def write_u16_plane(plane: av.video.plane.VideoPlane, src: np.ndarray, fill_value: int | None = None) -> None:
+    """Copy ``src`` into a uint16 plane respecting FFmpeg line padding."""
+    height, width = src.shape
+    stride_u16 = plane.line_size // np.dtype(np.uint16).itemsize
+    dst = np.frombuffer(plane, dtype=np.uint16).reshape(height, stride_u16)
+    if fill_value is not None:
+        dst.fill(fill_value)
+    dst[:, :width] = src
+
+
 @functools.cache
 def get_codec(vcodec: str) -> av.codec.Codec | None:
     """PyAV write-mode ``Codec`` for *vcodec*, or ``None`` if unavailable."""